{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/alex/Dropbox/Papers Essex/FPA Revision/Final Submission/Log_FPA_Replication_War_Performance_Survival_FMs.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 Jun 2020, 21:54:26
{txt}
{com}. 
. /**********************************************/
. /**************** REPICATION DO-FILE *************/
. /**********************************************/
. 
. use "/Users/alex/Dropbox/Papers Essex/FPA Revision/Final Submission/War_Performance_Survival_FMs_Replication_Data.dta",clear
{txt}
{com}. 
. 
. /*Data already stset*/
. stdescribe

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}{col 36}{c LT}{hline 14} per subject {hline 14}{c RT}
Category{col 28}total{col 41}mean{col 54}min{col 62}median{col 76}max
{hline 78}
no. of subjects       {res}      1087   
{txt}no. of records        {res}      1087           1           1          1          1

{txt}(first) entry time                 {res}        0           0          0          0
{txt}(final) exit time                  {res} 802.5299           1        417      14482

{txt}subjects with gap     {res}         0   
{txt}time on gap if gap    {res}         0   
{txt}time at risk          {res}    872350    802.5299           1        417      14482

{txt}failures              {res}       341    .3137075           0          0          1
{txt}{hline 78}

{com}. 
. 
. ******************************************
. ************Figure 1**********************
. ******************************************
. graph hbar (percent) ccode2,over(sex) ytitle("Gender") xsize(12) name(sex)
{res}{txt}
{com}. graph hbar (percent) ccode2,over(agegroup) ytitle("Age") xsize(12) name(age)
{res}{txt}
{com}. graph hbar (percent) ccode2,over(education2) ytitle("Education") xsize(12) name(educ)
{res}{txt}
{com}. graph hbar (percent) ccode2,over(milexp) ytitle("Military experience") xsize(12) name(mil, replace)
{res}{txt}
{com}. graph hbar (percent) ccode2,over(dipexp) ytitle("Diplomatic experience") xsize(12) name(dip, replace)
{res}{txt}
{com}. graph hbar (percent) ccode2,over(politics2) ytitle("Political experience") xsize(12) name(pol)
{res}{txt}
{com}. graph combine age educ sex mil dip pol,col(2)
{res}{txt}
{com}. 
. ******************************************
. ************Table 1***********************
. ******************************************
. preserve
{txt}
{com}. gsort -tenure_month
{txt}
{com}. list foreignminister country_name2 datein_str dateout_str tenure_month in 1/10
{txt}
     {c TLC}{hline 51}{c -}{hline 17}{c -}{hline 12}{c -}{hline 11}{c -}{hline 10}{c TRC}
     {c |} {res}                                  foreignminister     country_name2   datein_str   dateout~r   tenure~h {txt}{c |}
     {c LT}{hline 51}{c -}{hline 17}{c -}{hline 12}{c -}{hline 11}{c -}{hline 10}{c RT}
  1. {c |} {res}                      Karl Vasilyevich Nesselrode       Russia/USSR    1816-8-21   1856-4-15      14482 {txt}{c |}
  2. {c |} {res}                       Andrey Andreyevich Gromyko       Russia/USSR    1957-2-15    1985-7-2      10364 {txt}{c |}
  3. {c |} {res}Otto Eduard Leopold Graf von Bismarck-Schönhausen   Prussia/Germany   1862-11-23   1890-3-20       9979 {txt}{c |}
  4. {c |} {res}                 Aleksandr Mikhailovich Gorchakov       Russia/USSR    1856-4-27    1882-4-9       9478 {txt}{c |}
  5. {c |} {res}                  Aixin-Jueluo Yiyin 爱新觉罗奕忻             China    1861-1-20    1884-4-8       8479 {txt}{c |}
     {c LT}{hline 51}{c -}{hline 17}{c -}{hline 12}{c -}{hline 11}{c -}{hline 10}{c RT}
  6. {c |} {res}                Aixin-Jueluo Yikuang 爱新觉罗奕劻             China    1884-4-12   1901-7-24       6311 {txt}{c |}
  7. {c |} {res}                                      Osten Unden            Sweden    1945-8-12   1962-9-20       6248 {txt}{c |}
  8. {c |} {res}         Johan Gijsbert baron Verstolk van Soelen       Netherlands    1825-12-1   1841-9-13       5765 {txt}{c |}
  9. {c |} {res}                               Lars von Engestrom            Sweden    1809-3-15    1824-6-8       5564 {txt}{c |}
 10. {c |} {res}           Jose Moñino y Redondo de Floridablanca             Spain    1777-2-25   1792-2-28       5481 {txt}{c |}
     {c BLC}{hline 51}{c -}{hline 17}{c -}{hline 12}{c -}{hline 11}{c -}{hline 10}{c BRC}

{com}. restore
{txt}
{com}. 
. ******************************************
. ******************Table 2*****************
. ******************************************
. tab exit, miss

                           {txt}exit {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
         Death by natural cause {c |}{res}         22        1.99        1.99
{txt}Retirement/unforced resignation {c |}{res}        196       17.77       19.76
{txt}                      Violently {c |}{res}         37        3.35       23.12
{txt}              End of government {c |}{res}        427       38.71       61.83
{txt}             Forced resignation {c |}{res}        341       30.92       92.75
{txt}                      Incumbent {c |}{res}         12        1.09       93.83
{txt}                        Unknown {c |}{res}         68        6.17      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}      1,103      100.00
{txt}
{com}. 
. ******************************************
. ******************Figure 2****************
. ******************************************
. 
. *REPLICATION NOTE: The order of the countries produced by the code below*
. *for the top-left graph (Mean Hostility Level) of Figure 2 is slightly*
. *different than the order presented in the manuscript. The information*
. *is identical but the order of countries is varies slightly.*
.  
. graph hbar (mean) maxhostlev, over(ccode2) legend(size(small)) ytitle("Mean Hostility Level") name(context1, replace)
{res}{txt}
{com}. graph hbar, over(win) legend(size(small)) ytitle("Foreign Ministers with MID Victory") name(context2, replace)
{res}{txt}
{com}. graph hbar, over(lose) legend(size(small)) ytitle("Foreign Ministers with MID Loss") name(context3, replace)
{res}{txt}
{com}. graph hbar, over(compromise) legend(size(small)) ytitle("Foreign Ministers with MID Compromise") name(context4, replace)
{res}{txt}
{com}. graph combine context1 context2 context3 context4, title("Situational Conflict Variables: Percentages")
{res}{txt}
{com}. 
. 
. ******************************************
. ******************Table 3*****************
. ******************************************
. 
. ******************Model 1******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1560.5402{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1560.4812{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1560.4812{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.9545
{txt}Iteration 1:   log likelihood = {res}-1578.3431
{txt}Iteration 2:   log likelihood = {res}-1560.7792
{txt}Iteration 3:   log likelihood = {res}-1560.4817
{txt}Iteration 4:   log likelihood = {res}-1560.4812
{txt}Iteration 5:   log likelihood = {res}-1560.4812
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1560.4812

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}9{txt}){col 67}= {col 70}{res}    36.44
{txt}Log likelihood  =   {res}-1560.4812{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.227865{col 27}{space 2} .1757872{col 38}{space 1}    1.43{col 47}{space 3}0.152{col 55}{space 4} .9274458{col 68}{space 3} 1.625597
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.650704{col 27}{space 2} .4506438{col 38}{space 1}    1.84{col 47}{space 3}0.066{col 55}{space 4} .9666969{col 68}{space 3} 2.818695
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6569319{col 27}{space 2} .0922669{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .4988478{col 68}{space 3} .8651126
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} .9402858{col 27}{space 2} .1358203{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4} .7084471{col 68}{space 3} 1.247993
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.199476{col 27}{space 2} .5618331{col 38}{space 1}    0.39{col 47}{space 3}0.698{col 55}{space 4} .4789497{col 68}{space 3} 3.003954
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.012699{col 27}{space 2} .0078628{col 38}{space 1}    1.63{col 47}{space 3}0.104{col 55}{space 4} .9974049{col 68}{space 3} 1.028228
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.037343{col 27}{space 2}  .192616{col 38}{space 1}    0.20{col 47}{space 3}0.843{col 55}{space 4}  .720891{col 68}{space 3}  1.49271
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .8250654{col 27}{space 2} .0574004{col 38}{space 1}   -2.76{col 47}{space 3}0.006{col 55}{space 4} .7198958{col 68}{space 3} .9455992
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9605875{col 27}{space 2} .0112275{col 38}{space 1}   -3.44{col 47}{space 3}0.001{col 55}{space 4} .9388322{col 68}{space 3} .9828469
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .5274129   .2127642
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}112.73{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. ******************Model 2******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-724.72044{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-724.71854{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-724.71854{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-771.04586
{txt}Iteration 1:   log likelihood = {res}-728.67231
{txt}Iteration 2:   log likelihood = {res}-724.79936
{txt}Iteration 3:   log likelihood = {res}-724.71863
{txt}Iteration 4:   log likelihood = {res}-724.71854
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-724.71854

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       507
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         507{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         144{col 63}{txt}avg = {res} 36.214286
{txt}Time at risk    = {res}      524235{col 63}{txt}max = {res}        85

{col 49}{txt}Wald chi2({res}9{txt}){col 67}= {col 70}{res}    21.33
{txt}Log likelihood  =   {res}-724.71854{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0113

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.292948{col 27}{space 2} .2528884{col 38}{space 1}    1.31{col 47}{space 3}0.189{col 55}{space 4} .8812399{col 68}{space 3} 1.897003
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.223539{col 27}{space 2} .4767721{col 38}{space 1}    0.52{col 47}{space 3}0.605{col 55}{space 4} .5700767{col 68}{space 3} 2.626047
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .5341883{col 27}{space 2} .1120227{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .3541539{col 68}{space 3} .8057434
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.225091{col 27}{space 2} .2295301{col 38}{space 1}    1.08{col 47}{space 3}0.279{col 55}{space 4} .8485735{col 68}{space 3} 1.768673
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.374189{col 27}{space 2} 1.013465{col 38}{space 1}    0.43{col 47}{space 3}0.666{col 55}{space 4} .3238058{col 68}{space 3} 5.831876
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.020372{col 27}{space 2} .0123841{col 38}{space 1}    1.66{col 47}{space 3}0.097{col 55}{space 4} .9963862{col 68}{space 3} 1.044935
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .7333565{col 27}{space 2} .2071186{col 38}{space 1}   -1.10{col 47}{space 3}0.272{col 55}{space 4} .4216131{col 68}{space 3} 1.275605
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .8525156{col 27}{space 2} .0869636{col 38}{space 1}   -1.56{col 47}{space 3}0.118{col 55}{space 4} .6980279{col 68}{space 3} 1.041194
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9610964{col 27}{space 2} .0169962{col 38}{space 1}   -2.24{col 47}{space 3}0.025{col 55}{space 4}  .928355{col 68}{space 3} .9949924
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .8015923   .3750165
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}48.51{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. ******************Model 3******************
. stcox polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -1684.205{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1684.0486{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1684.0486{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -1835.858
{txt}Iteration 1:   log likelihood = {res}-1702.4489
{txt}Iteration 2:   log likelihood = {res}-1684.2642
{txt}Iteration 3:   log likelihood = {res}-1684.0487
{txt}Iteration 4:   log likelihood = {res}-1684.0486
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1684.0486

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       988
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         988{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         308{col 63}{txt}avg = {res} 70.571429
{txt}Time at risk    = {res}      761799{col 63}{txt}max = {res}       174

{col 49}{txt}Wald chi2({res}7{txt}){col 67}= {col 70}{res}   127.67
{txt}Log likelihood  =   {res}-1684.0486{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |} Haz. Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}polity2 {c |}{col 14}{res}{space 2} .9748873{col 26}{space 2} .0097051{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}   .95605{col 67}{space 3} .9940958
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} 1.270551{col 26}{space 2} .0641283{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} 1.150879{col 67}{space 3} 1.402668
{txt}{space 1}anyconflict {c |}{col 14}{res}{space 2} .8466107{col 26}{space 2} .2053462{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4} .5262887{col 67}{space 3} 1.361895
{txt}{space 9}win {c |}{col 14}{res}{space 2} .4292092{col 26}{space 2} .0911554{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4} .2830681{col 67}{space 3} .6507992
{txt}{space 8}lose {c |}{col 14}{res}{space 2} .7732673{col 26}{space 2} .1585792{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} .5173303{col 67}{space 3} 1.155823
{txt}{space 2}compromise {c |}{col 14}{res}{space 2} .6764576{col 26}{space 2} .1806541{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4} .4007924{col 67}{space 3} 1.141725
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2} .4067087{col 26}{space 2} .0974164{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4} .2543317{col 67}{space 3} .6503788
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       theta {c |}  {res} .4817929   .1951995
{txt}{hline 13}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}116.48{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. ******************Model 4******************
. stcox polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-755.94743{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-755.94735{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-755.94734{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -847.8545
{txt}Iteration 1:   log likelihood = {res}-782.86694
{txt}Iteration 2:   log likelihood = {res}-756.62188
{txt}Iteration 3:   log likelihood = {res} -755.9502
{txt}Iteration 4:   log likelihood = {res}-755.94734
{txt}Iteration 5:   log likelihood = {res}-755.94734
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-755.94734

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       538
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         538{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         157{col 63}{txt}avg = {res} 38.428571
{txt}Time at risk    = {res}      551837{col 63}{txt}max = {res}        96

{col 49}{txt}Wald chi2({res}6{txt}){col 67}= {col 70}{res}   113.46
{txt}Log likelihood  =   {res}-755.94734{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |} Haz. Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}polity2 {c |}{col 14}{res}{space 2} .9859758{col 26}{space 2} .0142948{col 37}{space 1}   -0.97{col 46}{space 3}0.330{col 54}{space 4} .9583528{col 67}{space 3} 1.014395
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} 1.319913{col 26}{space 2} .0695482{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} 1.190404{col 67}{space 3} 1.463512
{txt}{space 9}win {c |}{col 14}{res}{space 2} .3498674{col 26}{space 2} .0771405{col 37}{space 1}   -4.76{col 46}{space 3}0.000{col 54}{space 4} .2271049{col 67}{space 3} .5389898
{txt}{space 8}lose {c |}{col 14}{res}{space 2} .6705535{col 26}{space 2} .1410679{col 37}{space 1}   -1.90{col 46}{space 3}0.057{col 54}{space 4} .4439782{col 67}{space 3} 1.012757
{txt}{space 2}compromise {c |}{col 14}{res}{space 2} .5774259{col 26}{space 2} .1570412{col 37}{space 1}   -2.02{col 46}{space 3}0.043{col 54}{space 4} .3388417{col 67}{space 3} .9840011
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2} .3394202{col 26}{space 2} .0849018{col 37}{space 1}   -4.32{col 46}{space 3}0.000{col 54}{space 4} .2078835{col 67}{space 3} .5541859
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       theta {c |}  {res} .7838236   .3748857
{txt}{hline 13}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}47.82{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. ******************Model 5******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1500.2752{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1500.1726{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1500.1725{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.6393
{txt}Iteration 1:   log likelihood = {res} -1519.031
{txt}Iteration 2:   log likelihood = {res}-1500.4473
{txt}Iteration 3:   log likelihood = {res}-1500.1727
{txt}Iteration 4:   log likelihood = {res}-1500.1725
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1500.1725

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}   143.73
{txt}Log likelihood  =   {res}-1500.1725{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.304525{col 27}{space 2} .1901437{col 38}{space 1}    1.82{col 47}{space 3}0.068{col 55}{space 4} .9803556{col 68}{space 3} 1.735885
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 2.355922{col 27}{space 2}  .677529{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} 1.340811{col 68}{space 3}  4.13956
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6760982{col 27}{space 2} .0934557{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4} .5156439{col 68}{space 3} .8864815
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.034429{col 27}{space 2} .1510073{col 38}{space 1}    0.23{col 47}{space 3}0.817{col 55}{space 4} .7770359{col 68}{space 3} 1.377083
{txt}{space 7}female {c |}{col 15}{res}{space 2}  1.13146{col 27}{space 2}  .527969{col 38}{space 1}    0.26{col 47}{space 3}0.791{col 55}{space 4}  .453363{col 68}{space 3} 2.823789
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.008962{col 27}{space 2} .0076522{col 38}{space 1}    1.18{col 47}{space 3}0.239{col 55}{space 4} .9940747{col 68}{space 3} 1.024072
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.142927{col 27}{space 2}  .218832{col 38}{space 1}    0.70{col 47}{space 3}0.485{col 55}{space 4} .7853119{col 68}{space 3} 1.663393
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  .822038{col 27}{space 2} .0591361{col 38}{space 1}   -2.72{col 47}{space 3}0.006{col 55}{space 4} .7139337{col 68}{space 3} .9465116
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9665297{col 27}{space 2} .0110131{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .9451837{col 68}{space 3} .9883577
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2}  1.32641{col 27}{space 2} .0724645{col 38}{space 1}    5.17{col 47}{space 3}0.000{col 55}{space 4} 1.191722{col 68}{space 3} 1.476321
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .7282609{col 27}{space 2}  .187134{col 38}{space 1}   -1.23{col 47}{space 3}0.217{col 55}{space 4}  .440111{col 68}{space 3} 1.205069
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4913114{col 27}{space 2}  .109074{col 38}{space 1}   -3.20{col 47}{space 3}0.001{col 55}{space 4} .3179694{col 68}{space 3} .7591516
{txt}{space 9}lose {c |}{col 15}{res}{space 2}  .768755{col 27}{space 2} .1656752{col 38}{space 1}   -1.22{col 47}{space 3}0.222{col 55}{space 4} .5039003{col 68}{space 3}  1.17282
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .5263131{col 27}{space 2} .1518892{col 38}{space 1}   -2.22{col 47}{space 3}0.026{col 55}{space 4} .2989477{col 68}{space 3}  .926602
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4142917{col 27}{space 2} .1042244{col 38}{space 1}   -3.50{col 47}{space 3}0.000{col 55}{space 4} .2530272{col 68}{space 3} .6783366
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res}   .50201   .2073325
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}93.30{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. ******************Model 6******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -677.8903{txt}  
Iteration 1:{col 16}log profile likelihood = {res} -677.8903{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-772.15705
{txt}Iteration 1:   log likelihood = {res}-706.16466
{txt}Iteration 2:   log likelihood = {res} -678.7505
{txt}Iteration 3:   log likelihood = {res}-677.89127
{txt}Iteration 4:   log likelihood = {res} -677.8903
{txt}Refining estimates:
Iteration 0:   log likelihood = {res} -677.8903

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       507
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         507{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         144{col 63}{txt}avg = {res} 36.214286
{txt}Time at risk    = {res}      524235{col 63}{txt}max = {res}        85

{col 49}{txt}Wald chi2({res}14{txt}){col 67}= {col 70}{res}   114.03
{txt}Log likelihood  =   {res} -677.8903{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.291856{col 27}{space 2} .2564937{col 38}{space 1}    1.29{col 47}{space 3}0.197{col 55}{space 4} .8754091{col 68}{space 3} 1.906415
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.281336{col 27}{space 2} .5182397{col 38}{space 1}    0.61{col 47}{space 3}0.540{col 55}{space 4} .5799525{col 68}{space 3}  2.83096
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .5353217{col 27}{space 2}   .11276{col 38}{space 1}   -2.97{col 47}{space 3}0.003{col 55}{space 4} .3542566{col 68}{space 3} .8089315
{txt}{space 7}milexp {c |}{col 15}{res}{space 2}  1.23107{col 27}{space 2} .2341299{col 38}{space 1}    1.09{col 47}{space 3}0.274{col 55}{space 4} .8480042{col 68}{space 3} 1.787178
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.052681{col 27}{space 2} .7815771{col 38}{space 1}    0.07{col 47}{space 3}0.945{col 55}{space 4} .2456467{col 68}{space 3} 4.511103
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.010158{col 27}{space 2} .0124684{col 38}{space 1}    0.82{col 47}{space 3}0.413{col 55}{space 4} .9860135{col 68}{space 3} 1.034893
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .7629564{col 27}{space 2} .2312819{col 38}{space 1}   -0.89{col 47}{space 3}0.372{col 55}{space 4} .4211795{col 68}{space 3} 1.382077
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .7720681{col 27}{space 2} .0818764{col 38}{space 1}   -2.44{col 47}{space 3}0.015{col 55}{space 4} .6271728{col 68}{space 3} .9504383
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9711065{col 27}{space 2} .0168002{col 38}{space 1}   -1.69{col 47}{space 3}0.090{col 55}{space 4} .9387307{col 68}{space 3} 1.004599
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.374319{col 27}{space 2} .0796658{col 38}{space 1}    5.49{col 47}{space 3}0.000{col 55}{space 4}  1.22672{col 68}{space 3} 1.539676
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4236912{col 27}{space 2} .0994014{col 38}{space 1}   -3.66{col 47}{space 3}0.000{col 55}{space 4} .2675166{col 68}{space 3} .6710397
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .6586043{col 27}{space 2} .1489797{col 38}{space 1}   -1.85{col 47}{space 3}0.065{col 55}{space 4} .4227448{col 68}{space 3} 1.026055
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .4894078{col 27}{space 2} .1492958{col 38}{space 1}   -2.34{col 47}{space 3}0.019{col 55}{space 4} .2691587{col 68}{space 3} .8898837
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .3301941{col 27}{space 2} .0878024{col 38}{space 1}   -4.17{col 47}{space 3}0.000{col 55}{space 4} .1960763{col 68}{space 3} .5560498
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .9693877   .4401124
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}50.77{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. 
. ******************************************
. ******************Table 4*****************
. ******************************************
. 
. ******************Model 7******************
. stcox previous_term politics2 vdem_politics2 dipexp vdem_dipexp milexp female agein hog education2 polity2 maxhostlev vdem_maxhostlev anyconflict win vdem_win lose vdem_lose compromise vdem_comp Any_MID_End, shared(ccode) 

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1498.7433{txt}  
Iteration 1:{col 16}log profile likelihood = {res} -1498.642{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1498.6419{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.6432
{txt}Iteration 1:   log likelihood = {res}-1523.0086
{txt}Iteration 2:   log likelihood = {res}-1499.3204
{txt}Iteration 3:   log likelihood = {res}-1498.6426
{txt}Iteration 4:   log likelihood = {res}-1498.6419
{txt}Iteration 5:   log likelihood = {res}-1498.6419
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1498.6419

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}21{txt}){col 67}= {col 70}{res}   147.43
{txt}Log likelihood  =   {res}-1498.6419{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             _t{col 17}{c |} Haz. Ratio{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}previous_term {c |}{col 17}{res}{space 2} 1.283499{col 29}{space 2} .1882004{col 40}{space 1}    1.70{col 49}{space 3}0.089{col 57}{space 4}  .962905{col 70}{space 3} 1.710834
{txt}{space 6}politics2 {c |}{col 17}{res}{space 2} 2.295605{col 29}{space 2} .8248386{col 40}{space 1}    2.31{col 49}{space 3}0.021{col 57}{space 4} 1.135142{col 70}{space 3} 4.642417
{txt}{space 1}vdem_politics2 {c |}{col 17}{res}{space 2} .9891986{col 29}{space 2} .0561527{col 40}{space 1}   -0.19{col 49}{space 3}0.848{col 57}{space 4} .8850428{col 70}{space 3} 1.105612
{txt}{space 9}dipexp {c |}{col 17}{res}{space 2} .6777478{col 29}{space 2} .0967775{col 40}{space 1}   -2.72{col 49}{space 3}0.006{col 57}{space 4} .5122981{col 70}{space 3} .8966305
{txt}{space 4}vdem_dipexp {c |}{col 17}{res}{space 2} .9782167{col 29}{space 2} .0199861{col 40}{space 1}   -1.08{col 49}{space 3}0.281{col 57}{space 4} .9398186{col 70}{space 3} 1.018184
{txt}{space 9}milexp {c |}{col 17}{res}{space 2} 1.043904{col 29}{space 2}  .154212{col 40}{space 1}    0.29{col 49}{space 3}0.771{col 57}{space 4} .7814766{col 70}{space 3} 1.394458
{txt}{space 9}female {c |}{col 17}{res}{space 2} 1.145873{col 29}{space 2} .5369803{col 40}{space 1}    0.29{col 49}{space 3}0.771{col 57}{space 4} .4573464{col 70}{space 3} 2.870963
{txt}{space 10}agein {c |}{col 17}{res}{space 2} 1.008365{col 29}{space 2} .0076814{col 40}{space 1}    1.09{col 49}{space 3}0.274{col 57}{space 4} .9934216{col 70}{space 3} 1.023533
{txt}{space 12}hog {c |}{col 17}{res}{space 2} 1.158097{col 29}{space 2} .2245367{col 40}{space 1}    0.76{col 49}{space 3}0.449{col 57}{space 4} .7919735{col 70}{space 3} 1.693478
{txt}{space 5}education2 {c |}{col 17}{res}{space 2} .8267009{col 29}{space 2} .0601672{col 40}{space 1}   -2.61{col 49}{space 3}0.009{col 57}{space 4} .7168001{col 70}{space 3} .9534518
{txt}{space 8}polity2 {c |}{col 17}{res}{space 2} .9812489{col 29}{space 2} .0556253{col 40}{space 1}   -0.33{col 49}{space 3}0.738{col 57}{space 4} .8780638{col 70}{space 3}  1.09656
{txt}{space 5}maxhostlev {c |}{col 17}{res}{space 2} 1.329129{col 29}{space 2} .0735181{col 40}{space 1}    5.14{col 49}{space 3}0.000{col 57}{space 4} 1.192572{col 70}{space 3} 1.481323
{txt}vdem_maxhostlev {c |}{col 17}{res}{space 2} .9975302{col 29}{space 2} .0056185{col 40}{space 1}   -0.44{col 49}{space 3}0.661{col 57}{space 4} .9865787{col 70}{space 3} 1.008603
{txt}{space 4}anyconflict {c |}{col 17}{res}{space 2} .7383301{col 29}{space 2} .1914149{col 40}{space 1}   -1.17{col 49}{space 3}0.242{col 57}{space 4} .4441947{col 70}{space 3} 1.227235
{txt}{space 12}win {c |}{col 17}{res}{space 2} .4722969{col 29}{space 2} .1096998{col 40}{space 1}   -3.23{col 49}{space 3}0.001{col 57}{space 4} .2995764{col 70}{space 3} .7445993
{txt}{space 7}vdem_win {c |}{col 17}{res}{space 2} 1.026002{col 29}{space 2} .0287489{col 40}{space 1}    0.92{col 49}{space 3}0.360{col 57}{space 4} .9711743{col 70}{space 3} 1.083925
{txt}{space 11}lose {c |}{col 17}{res}{space 2}  .778329{col 29}{space 2} .1725898{col 40}{space 1}   -1.13{col 49}{space 3}0.258{col 57}{space 4} .5039812{col 70}{space 3} 1.202021
{txt}{space 6}vdem_lose {c |}{col 17}{res}{space 2} 1.012896{col 29}{space 2} .0267373{col 40}{space 1}    0.49{col 49}{space 3}0.627{col 57}{space 4} .9618243{col 70}{space 3} 1.066679
{txt}{space 5}compromise {c |}{col 17}{res}{space 2} .5266987{col 29}{space 2} .1518091{col 40}{space 1}   -2.22{col 49}{space 3}0.026{col 57}{space 4} .2993798{col 70}{space 3} .9266207
{txt}{space 6}vdem_comp {c |}{col 17}{res}{space 2} 1.018505{col 29}{space 2} .0364082{col 40}{space 1}    0.51{col 49}{space 3}0.608{col 57}{space 4} .9495883{col 70}{space 3} 1.092423
{txt}{space 4}Any_MID_End {c |}{col 17}{res}{space 2} .3969374{col 29}{space 2} .1021522{col 40}{space 1}   -3.59{col 49}{space 3}0.000{col 57}{space 4}  .239698{col 70}{space 3}  .657324
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          theta {c |}  {res} .5023149   .2081856
{txt}{hline 16}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}90.37{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. 
. ******************************************
. ******************Appendix 1**************
. ******************************************
. 
. ******************************************
. ******************Table A1****************
. ******************************************
. 
. *REPLICATION NOTE: The code below produces both estimation results with 'streg'*
. *and a RTF of Table A1. The 'streg' code produces coefficients for *
. *six Weibull models, and the 'eststo' and 'esttab' code produces their*
. *exponentiated versions presented in Table A1. Unlike the other replication*
. *code presented in this do-file, we choose to present code for coefficients and*
. *code for Table A1 because Stata 15 does not produce exponentiated*
. *coefficients for this type of specification with the modelling of an ancilliary*
. *parameter (Stata 15 Manual p. 293). Therefore, readers interested in*
. *replicating the estimates precisely as presented in Table A1, should install*
. *the third-party ado-files for 'eststo' and 'esttab' and run the code below.*
. *Readers interested simply on the coefficients do not need to run the *
. *third-party ado-files and may focus only on the 'streg' code.*
. *Substantive interpretation of results is equivalent across versions.*
. 
. ******************Model 1******************
. streg previous_term politics2 dipexp milexp female agein hog education2 polity2, d(w) anc(polity2) hr

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-825.56011}  
Iteration 1:{space 3}log likelihood = {res: -820.5348}  
Iteration 2:{space 3}log likelihood = {res:-820.30478}  
Iteration 3:{space 3}log likelihood = {res:-820.30468}  
Iteration 4:{space 3}log likelihood = {res:-820.30468}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-820.30468}  
Iteration 1:{space 3}log likelihood = {res:-797.99769}  
Iteration 2:{space 3}log likelihood = {res:-795.07163}  
Iteration 3:{space 3}log likelihood = {res:-795.06629}  
Iteration 4:{space 3}log likelihood = {res:-795.06629}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         912                  {txt}Number of obs    =  {res}       912
{txt}No. of failures = {res}         282
{txt}Time at risk    = {res}      715779
                                                {txt}LR chi2({res}9{txt})       =  {res}     50.48
{txt}Log likelihood  =   {res}-795.06629                  {txt}Prob > chi2      =  {res}    0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t            {txt}{c |}
previous_term {c |}{col 15}{res}{space 2} .4141761{col 27}{space 2} .1308358{col 38}{space 1}    3.17{col 47}{space 3}0.002{col 55}{space 4} .1577426{col 68}{space 3} .6706096
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2}  .431075{col 27}{space 2} .2637885{col 38}{space 1}    1.63{col 47}{space 3}0.102{col 55}{space 4}-.0859409{col 68}{space 3}  .948091
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2}-.4061999{col 27}{space 2} .1377026{col 38}{space 1}   -2.95{col 47}{space 3}0.003{col 55}{space 4}-.6760921{col 68}{space 3}-.1363078
{txt}{space 7}milexp {c |}{col 15}{res}{space 2}-.1761034{col 27}{space 2} .1369917{col 38}{space 1}   -1.29{col 47}{space 3}0.199{col 55}{space 4}-.4446023{col 68}{space 3} .0923955
{txt}{space 7}female {c |}{col 15}{res}{space 2} .0070588{col 27}{space 2}  .461347{col 38}{space 1}    0.02{col 47}{space 3}0.988{col 55}{space 4}-.8971647{col 68}{space 3} .9112823
{txt}{space 8}agein {c |}{col 15}{res}{space 2}  .007873{col 27}{space 2}  .007269{col 38}{space 1}    1.08{col 47}{space 3}0.279{col 55}{space 4}-.0063739{col 68}{space 3} .0221199
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .2526537{col 27}{space 2} .1704886{col 38}{space 1}    1.48{col 47}{space 3}0.138{col 55}{space 4}-.0814977{col 68}{space 3} .5868051
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  -.12481{col 27}{space 2} .0589394{col 38}{space 1}   -2.12{col 47}{space 3}0.034{col 55}{space 4}-.2403291{col 68}{space 3}-.0092908
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2}-.1036836{col 27}{space 2} .0319134{col 38}{space 1}   -3.25{col 47}{space 3}0.001{col 55}{space 4}-.1662327{col 68}{space 3}-.0411346
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-6.948026{col 27}{space 2} .5935446{col 38}{space 1}  -11.71{col 47}{space 3}0.000{col 55}{space 4}-8.111352{col 68}{space 3}  -5.7847
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p          {txt}{c |}
{space 6}polity2 {c |}{col 15}{res}{space 2} .0110309{col 27}{space 2} .0049275{col 38}{space 1}    2.24{col 47}{space 3}0.025{col 55}{space 4} .0013733{col 68}{space 3} .0206886
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.1725212{col 27}{space 2} .0449461{col 38}{space 1}   -3.84{col 47}{space 3}0.000{col 55}{space 4}-.2606139{col 68}{space 3}-.0844285
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m1eh
{txt}
{com}. ******************Model 2******************
. streg previous_term politics2 dipexp milexp female agein hog education2 polity2 if anyconflict==1, d(w) anc(polity2)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-425.58227}  
Iteration 1:{space 3}log likelihood = {res:-425.35986}  
Iteration 2:{space 3}log likelihood = {res:-425.35909}  
Iteration 3:{space 3}log likelihood = {res:-425.35909}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-425.35909}  
Iteration 1:{space 3}log likelihood = {res:-416.04254}  
Iteration 2:{space 3}log likelihood = {res: -415.2869}  
Iteration 3:{space 3}log likelihood = {res:-415.28452}  
Iteration 4:{space 3}log likelihood = {res:-415.28452}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         507                  {txt}Number of obs    =  {res}       507
{txt}No. of failures = {res}         144
{txt}Time at risk    = {res}      524235
                                                {txt}LR chi2({res}9{txt})       =  {res}     20.15
{txt}Log likelihood  =   {res}-415.28452                  {txt}Prob > chi2      =  {res}    0.0170

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t            {txt}{c |}
previous_term {c |}{col 15}{res}{space 2} .4205273{col 27}{space 2} .1775733{col 38}{space 1}    2.37{col 47}{space 3}0.018{col 55}{space 4}   .07249{col 68}{space 3} .7685646
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} .2407089{col 27}{space 2}  .374417{col 38}{space 1}    0.64{col 47}{space 3}0.520{col 55}{space 4} -.493135{col 68}{space 3} .9745528
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2}-.3983751{col 27}{space 2} .1972633{col 38}{space 1}   -2.02{col 47}{space 3}0.043{col 55}{space 4}-.7850041{col 68}{space 3} -.011746
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} .1367678{col 27}{space 2} .1785126{col 38}{space 1}    0.77{col 47}{space 3}0.444{col 55}{space 4}-.2131103{col 68}{space 3}  .486646
{txt}{space 7}female {c |}{col 15}{res}{space 2}-.0673986{col 27}{space 2} .7248908{col 38}{space 1}   -0.09{col 47}{space 3}0.926{col 55}{space 4}-1.488158{col 68}{space 3} 1.353361
{txt}{space 8}agein {c |}{col 15}{res}{space 2} .0160598{col 27}{space 2} .0109493{col 38}{space 1}    1.47{col 47}{space 3}0.142{col 55}{space 4}-.0054004{col 68}{space 3}   .03752
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .2254925{col 27}{space 2} .2434007{col 38}{space 1}    0.93{col 47}{space 3}0.354{col 55}{space 4}-.2515641{col 68}{space 3} .7025492
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}-.0611248{col 27}{space 2} .0910729{col 38}{space 1}   -0.67{col 47}{space 3}0.502{col 55}{space 4}-.2396245{col 68}{space 3} .1173748
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2}-.0829208{col 27}{space 2} .0506259{col 38}{space 1}   -1.64{col 47}{space 3}0.101{col 55}{space 4}-.1821457{col 68}{space 3} .0163041
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-8.277479{col 27}{space 2} .9215744{col 38}{space 1}   -8.98{col 47}{space 3}0.000{col 55}{space 4}-10.08373{col 68}{space 3}-6.471226
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p          {txt}{c |}
{space 6}polity2 {c |}{col 15}{res}{space 2} .0089969{col 27}{space 2} .0073233{col 38}{space 1}    1.23{col 47}{space 3}0.219{col 55}{space 4}-.0053564{col 68}{space 3} .0233502
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.1125996{col 27}{space 2} .0651743{col 38}{space 1}   -1.73{col 47}{space 3}0.084{col 55}{space 4} -.240339{col 68}{space 3} .0151397
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m2eh
{txt}
{com}. ******************Model 3******************
. streg polity2 maxhostlev anyconflict win lose compromise Any_MID_End, d(w) anc(polity2)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-903.21186}  
Iteration 1:{space 3}log likelihood = {res:-897.67054}  
Iteration 2:{space 3}log likelihood = {res:-897.42039}  
Iteration 3:{space 3}log likelihood = {res:-897.42028}  
Iteration 4:{space 3}log likelihood = {res:-897.42028}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-897.42028}  
Iteration 1:{space 3}log likelihood = {res:-859.95418}  
Iteration 2:{space 3}log likelihood = {res:-817.35031}  
Iteration 3:{space 3}log likelihood = {res:-816.83147}  
Iteration 4:{space 3}log likelihood = {res:-816.83045}  
Iteration 5:{space 3}log likelihood = {res:-816.83045}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         988                  {txt}Number of obs    =  {res}       988
{txt}No. of failures = {res}         308
{txt}Time at risk    = {res}      761799
                                                {txt}LR chi2({res}7{txt})       =  {res}    161.18
{txt}Log likelihood  =   {res}-816.83045                  {txt}Prob > chi2      =  {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t           {txt}{c |}
{space 5}polity2 {c |}{col 14}{res}{space 2}-.1088775{col 26}{space 2} .0330992{col 37}{space 1}   -3.29{col 46}{space 3}0.001{col 54}{space 4}-.1737508{col 67}{space 3}-.0440042
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} .2467758{col 26}{space 2} .0495354{col 37}{space 1}    4.98{col 46}{space 3}0.000{col 54}{space 4} .1496882{col 67}{space 3} .3438634
{txt}{space 1}anyconflict {c |}{col 14}{res}{space 2}-.0033582{col 26}{space 2} .2346307{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-.4632258{col 67}{space 3} .4565095
{txt}{space 9}win {c |}{col 14}{res}{space 2}-.7830098{col 26}{space 2} .2053554{col 37}{space 1}   -3.81{col 46}{space 3}0.000{col 54}{space 4}-1.185499{col 67}{space 3}-.3805207
{txt}{space 8}lose {c |}{col 14}{res}{space 2}-.2809309{col 26}{space 2} .1965005{col 37}{space 1}   -1.43{col 46}{space 3}0.153{col 54}{space 4}-.6660648{col 67}{space 3}  .104203
{txt}{space 2}compromise {c |}{col 14}{res}{space 2}-.5271853{col 26}{space 2} .2535639{col 37}{space 1}   -2.08{col 46}{space 3}0.038{col 54}{space 4}-1.024161{col 67}{space 3}-.0302091
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2}-.9389898{col 26}{space 2} .2306161{col 37}{space 1}   -4.07{col 46}{space 3}0.000{col 54}{space 4}-1.390989{col 67}{space 3}-.4869906
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-7.108673{col 26}{space 2} .2962305{col 37}{space 1}  -24.00{col 46}{space 3}0.000{col 54}{space 4}-7.689274{col 67}{space 3}-6.528071
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p         {txt}{c |}
{space 5}polity2 {c |}{col 14}{res}{space 2} .0104049{col 26}{space 2} .0045868{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4}  .001415{col 67}{space 3} .0193948
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0150777{col 26}{space 2} .0427847{col 37}{space 1}   -0.35{col 46}{space 3}0.725{col 54}{space 4}-.0989341{col 67}{space 3} .0687787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m3eh
{txt}
{com}. ******************Model 4******************
. streg polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, d(w) anc(polity2)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-461.04085}  
Iteration 1:{space 3}log likelihood = {res:-460.70747}  
Iteration 2:{space 3}log likelihood = {res:-460.70589}  
Iteration 3:{space 3}log likelihood = {res:-460.70589}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-460.70589}  
Iteration 1:{space 3}log likelihood = {res:-415.83337}  
Iteration 2:{space 3}log likelihood = {res:-403.82031}  
Iteration 3:{space 3}log likelihood = {res: -403.1557}  
Iteration 4:{space 3}log likelihood = {res:-403.12338}  
Iteration 5:{space 3}log likelihood = {res:-403.12335}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         538                  {txt}Number of obs    =  {res}       538
{txt}No. of failures = {res}         157
{txt}Time at risk    = {res}      551837
                                                {txt}LR chi2({res}6{txt})       =  {res}    115.17
{txt}Log likelihood  =   {res}-403.12335                  {txt}Prob > chi2      =  {res}    0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t           {txt}{c |}
{space 5}polity2 {c |}{col 14}{res}{space 2} -.078411{col 26}{space 2} .0510393{col 37}{space 1}   -1.54{col 46}{space 3}0.124{col 54}{space 4}-.1784463{col 67}{space 3} .0216242
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} .2860836{col 26}{space 2} .0509987{col 37}{space 1}    5.61{col 46}{space 3}0.000{col 54}{space 4}  .186128{col 67}{space 3} .3860392
{txt}{space 9}win {c |}{col 14}{res}{space 2}-.8663117{col 26}{space 2} .2086377{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-1.275234{col 67}{space 3}-.4573894
{txt}{space 8}lose {c |}{col 14}{res}{space 2}-.3177565{col 26}{space 2} .1991413{col 37}{space 1}   -1.60{col 46}{space 3}0.111{col 54}{space 4}-.7080663{col 67}{space 3} .0725532
{txt}{space 2}compromise {c |}{col 14}{res}{space 2}-.6101344{col 26}{space 2}  .259276{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-1.118306{col 67}{space 3}-.1019629
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2}-1.110953{col 26}{space 2}  .235259{col 37}{space 1}   -4.72{col 46}{space 3}0.000{col 54}{space 4}-1.572052{col 67}{space 3}-.6498537
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-8.316904{col 26}{space 2} .5191198{col 37}{space 1}  -16.02{col 46}{space 3}0.000{col 54}{space 4} -9.33436{col 67}{space 3}-7.299448
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p         {txt}{c |}
{space 5}polity2 {c |}{col 14}{res}{space 2} .0068131{col 26}{space 2} .0059052{col 37}{space 1}    1.15{col 46}{space 3}0.249{col 54}{space 4}-.0047609{col 67}{space 3}  .018387
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1564596{col 26}{space 2} .0609798{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .0369414{col 67}{space 3} .2759779
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m4eh
{txt}
{com}. ******************Model 5******************
. streg previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, d(w) anc(polity2)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-825.56011}  
Iteration 1:{space 3}log likelihood = {res: -820.5348}  
Iteration 2:{space 3}log likelihood = {res:-820.30478}  
Iteration 3:{space 3}log likelihood = {res:-820.30468}  
Iteration 4:{space 3}log likelihood = {res:-820.30468}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-820.30468}  
Iteration 1:{space 3}log likelihood = {res:-790.74028}  
Iteration 2:{space 3}log likelihood = {res:-722.20464}  
Iteration 3:{space 3}log likelihood = {res:-721.28846}  
Iteration 4:{space 3}log likelihood = {res:-721.28348}  
Iteration 5:{space 3}log likelihood = {res:-721.28348}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         912                  {txt}Number of obs    =  {res}       912
{txt}No. of failures = {res}         282
{txt}Time at risk    = {res}      715779
                                                {txt}LR chi2({res}15{txt})      =  {res}    198.04
{txt}Log likelihood  =   {res}-721.28348                  {txt}Prob > chi2      =  {res}    0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t            {txt}{c |}
previous_term {c |}{col 15}{res}{space 2}  .542322{col 27}{space 2} .1372863{col 38}{space 1}    3.95{col 47}{space 3}0.000{col 55}{space 4} .2732459{col 68}{space 3} .8113982
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} .4700308{col 27}{space 2}  .266532{col 38}{space 1}    1.76{col 47}{space 3}0.078{col 55}{space 4}-.0523624{col 68}{space 3}  .992424
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2}-.4312819{col 27}{space 2} .1391687{col 38}{space 1}   -3.10{col 47}{space 3}0.002{col 55}{space 4}-.7040475{col 68}{space 3}-.1585162
{txt}{space 7}milexp {c |}{col 15}{res}{space 2}-.0779131{col 27}{space 2} .1391326{col 38}{space 1}   -0.56{col 47}{space 3}0.575{col 55}{space 4} -.350608{col 68}{space 3} .1947818
{txt}{space 7}female {c |}{col 15}{res}{space 2}-.2149918{col 27}{space 2} .4617129{col 38}{space 1}   -0.47{col 47}{space 3}0.641{col 55}{space 4}-1.119933{col 68}{space 3} .6899489
{txt}{space 8}agein {c |}{col 15}{res}{space 2} .0073697{col 27}{space 2} .0072104{col 38}{space 1}    1.02{col 47}{space 3}0.307{col 55}{space 4}-.0067623{col 68}{space 3} .0215018
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .2779207{col 27}{space 2} .1759046{col 38}{space 1}    1.58{col 47}{space 3}0.114{col 55}{space 4} -.066846{col 68}{space 3} .6226875
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}-.1489231{col 27}{space 2} .0585118{col 38}{space 1}   -2.55{col 47}{space 3}0.011{col 55}{space 4}-.2636042{col 68}{space 3} -.034242
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2}-.0784599{col 27}{space 2} .0355712{col 38}{space 1}   -2.21{col 47}{space 3}0.027{col 55}{space 4}-.1481782{col 68}{space 3}-.0087416
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} .2596757{col 27}{space 2} .0525267{col 38}{space 1}    4.94{col 47}{space 3}0.000{col 55}{space 4} .1567252{col 68}{space 3} .3626262
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2}-.2160679{col 27}{space 2} .2484335{col 38}{space 1}   -0.87{col 47}{space 3}0.384{col 55}{space 4}-.7029887{col 68}{space 3} .2708528
{txt}{space 10}win {c |}{col 15}{res}{space 2}-.6739496{col 27}{space 2} .2161333{col 38}{space 1}   -3.12{col 47}{space 3}0.002{col 55}{space 4}-1.097563{col 68}{space 3}-.2503361
{txt}{space 9}lose {c |}{col 15}{res}{space 2}-.2269514{col 27}{space 2}  .205585{col 38}{space 1}   -1.10{col 47}{space 3}0.270{col 55}{space 4}-.6298906{col 68}{space 3} .1759878
{txt}{space 3}compromise {c |}{col 15}{res}{space 2}-.6696236{col 27}{space 2} .2709191{col 38}{space 1}   -2.47{col 47}{space 3}0.013{col 55}{space 4}-1.200615{col 68}{space 3}-.1386318
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2}-.9782448{col 27}{space 2} .2436196{col 38}{space 1}   -4.02{col 47}{space 3}0.000{col 55}{space 4} -1.45573{col 68}{space 3}-.5007592
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-7.806864{col 27}{space 2} .6138661{col 38}{space 1}  -12.72{col 47}{space 3}0.000{col 55}{space 4} -9.01002{col 68}{space 3}-6.603709
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p          {txt}{c |}
{space 6}polity2 {c |}{col 15}{res}{space 2} .0056743{col 27}{space 2} .0045895{col 38}{space 1}    1.24{col 47}{space 3}0.216{col 55}{space 4}-.0033209{col 68}{space 3} .0146695
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .0626806{col 27}{space 2} .0449361{col 38}{space 1}    1.39{col 47}{space 3}0.163{col 55}{space 4}-.0253926{col 68}{space 3} .1507538
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m5eh
{txt}
{com}. ******************Model 6******************
. streg previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, d(w) anc(polity2)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log likelihood = {res:-425.58227}  
Iteration 1:{space 3}log likelihood = {res:-425.35986}  
Iteration 2:{space 3}log likelihood = {res:-425.35909}  
Iteration 3:{space 3}log likelihood = {res:-425.35909}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-425.35909}  
Iteration 1:{space 3}log likelihood = {res:-381.97349}  
Iteration 2:{space 3}log likelihood = {res:-368.81436}  
Iteration 3:{space 3}log likelihood = {res:-368.10828}  
Iteration 4:{space 3}log likelihood = {res:-368.07999}  
Iteration 5:{space 3}log likelihood = {res:-368.07998}  
{res}
{txt}Weibull PH regression

No. of subjects = {res}         507                  {txt}Number of obs    =  {res}       507
{txt}No. of failures = {res}         144
{txt}Time at risk    = {res}      524235
                                                {txt}LR chi2({res}14{txt})      =  {res}    114.56
{txt}Log likelihood  =   {res}-368.07998                  {txt}Prob > chi2      =  {res}    0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_t            {txt}{c |}
previous_term {c |}{col 15}{res}{space 2} .3756673{col 27}{space 2} .1805016{col 38}{space 1}    2.08{col 47}{space 3}0.037{col 55}{space 4} .0218906{col 68}{space 3}  .729444
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} .0333747{col 27}{space 2} .3805781{col 38}{space 1}    0.09{col 47}{space 3}0.930{col 55}{space 4}-.7125447{col 68}{space 3}  .779294
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2}-.3916936{col 27}{space 2} .1998308{col 38}{space 1}   -1.96{col 47}{space 3}0.050{col 55}{space 4}-.7833547{col 68}{space 3}-.0000324
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} .2137237{col 27}{space 2} .1839622{col 38}{space 1}    1.16{col 47}{space 3}0.245{col 55}{space 4}-.1468356{col 68}{space 3} .5742829
{txt}{space 7}female {c |}{col 15}{res}{space 2}-.3793966{col 27}{space 2} .7265414{col 38}{space 1}   -0.52{col 47}{space 3}0.602{col 55}{space 4}-1.803391{col 68}{space 3} 1.044598
{txt}{space 8}agein {c |}{col 15}{res}{space 2} .0048111{col 27}{space 2} .0110309{col 38}{space 1}    0.44{col 47}{space 3}0.663{col 55}{space 4}-.0168092{col 68}{space 3} .0264313
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .1985475{col 27}{space 2} .2609496{col 38}{space 1}    0.76{col 47}{space 3}0.447{col 55}{space 4}-.3129042{col 68}{space 3} .7099993
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}-.0720251{col 27}{space 2} .0917066{col 38}{space 1}   -0.79{col 47}{space 3}0.432{col 55}{space 4}-.2517666{col 68}{space 3} .1077165
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} -.041683{col 27}{space 2} .0541716{col 38}{space 1}   -0.77{col 47}{space 3}0.442{col 55}{space 4}-.1478573{col 68}{space 3} .0644914
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2}  .279492{col 27}{space 2} .0541477{col 38}{space 1}    5.16{col 47}{space 3}0.000{col 55}{space 4} .1733644{col 68}{space 3} .3856195
{txt}{space 10}win {c |}{col 15}{res}{space 2}-.7536062{col 27}{space 2} .2205642{col 38}{space 1}   -3.42{col 47}{space 3}0.001{col 55}{space 4}-1.185904{col 68}{space 3}-.3213083
{txt}{space 9}lose {c |}{col 15}{res}{space 2}-.2537853{col 27}{space 2} .2066602{col 38}{space 1}   -1.23{col 47}{space 3}0.219{col 55}{space 4}-.6588318{col 68}{space 3} .1512613
{txt}{space 3}compromise {c |}{col 15}{res}{space 2}-.7738564{col 27}{space 2} .2829634{col 38}{space 1}   -2.73{col 47}{space 3}0.006{col 55}{space 4}-1.328454{col 68}{space 3}-.2192584
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} -1.18838{col 27}{space 2} .2528785{col 38}{space 1}   -4.70{col 47}{space 3}0.000{col 55}{space 4}-1.684012{col 68}{space 3}-.6927468
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-8.582217{col 27}{space 2} .9816694{col 38}{space 1}   -8.74{col 47}{space 3}0.000{col 55}{space 4}-10.50625{col 68}{space 3} -6.65818
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ln_p          {txt}{c |}
{space 6}polity2 {c |}{col 15}{res}{space 2} .0020376{col 27}{space 2} .0061117{col 38}{space 1}    0.33{col 47}{space 3}0.739{col 55}{space 4}-.0099411{col 68}{space 3} .0140163
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .1837241{col 27}{space 2} .0638887{col 38}{space 1}    2.88{col 47}{space 3}0.004{col 55}{space 4} .0585047{col 68}{space 3} .3089436
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. eststo m6eh
{txt}
{com}. 
. ******************Table A1******************
. esttab m1eh m2eh m3eh m4eh m5eh m6eh using TableA1.rtf, se parentheses nogaps obslast label mtitle("Model 1" "Model 2" "Model 3" "Model 4" "Model 5" "Model 6") star(* 0.1 ** 0.05 *** 0.01) r2 eform constant replace
{txt}(note: file TableA1.rtf not found)
(output written to {browse  `"TableA1.rtf"'})

{com}. 
. ******************************************
. ******************Appendix 2**************
. ******************************************
. 
. ******************Model 1******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1560.5402{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1560.4812{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1560.4812{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.9545
{txt}Iteration 1:   log likelihood = {res}-1578.3431
{txt}Iteration 2:   log likelihood = {res}-1560.7792
{txt}Iteration 3:   log likelihood = {res}-1560.4817
{txt}Iteration 4:   log likelihood = {res}-1560.4812
{txt}Iteration 5:   log likelihood = {res}-1560.4812
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1560.4812

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}9{txt}){col 67}= {col 70}{res}    36.44
{txt}Log likelihood  =   {res}-1560.4812{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.227865{col 27}{space 2} .1757872{col 38}{space 1}    1.43{col 47}{space 3}0.152{col 55}{space 4} .9274458{col 68}{space 3} 1.625597
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.650704{col 27}{space 2} .4506438{col 38}{space 1}    1.84{col 47}{space 3}0.066{col 55}{space 4} .9666969{col 68}{space 3} 2.818695
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6569319{col 27}{space 2} .0922669{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .4988478{col 68}{space 3} .8651126
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} .9402858{col 27}{space 2} .1358203{col 38}{space 1}   -0.43{col 47}{space 3}0.670{col 55}{space 4} .7084471{col 68}{space 3} 1.247993
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.199476{col 27}{space 2} .5618331{col 38}{space 1}    0.39{col 47}{space 3}0.698{col 55}{space 4} .4789497{col 68}{space 3} 3.003954
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.012699{col 27}{space 2} .0078628{col 38}{space 1}    1.63{col 47}{space 3}0.104{col 55}{space 4} .9974049{col 68}{space 3} 1.028228
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.037343{col 27}{space 2}  .192616{col 38}{space 1}    0.20{col 47}{space 3}0.843{col 55}{space 4}  .720891{col 68}{space 3}  1.49271
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .8250654{col 27}{space 2} .0574004{col 38}{space 1}   -2.76{col 47}{space 3}0.006{col 55}{space 4} .7198958{col 68}{space 3} .9455992
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9605875{col 27}{space 2} .0112275{col 38}{space 1}   -3.44{col 47}{space 3}0.001{col 55}{space 4} .9388322{col 68}{space 3} .9828469
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .5274129   .2127642
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}112.73{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.01120{col 37}     0.04{col 50}    1{col 64}0.8389
{txt}      politics2{col 19}{c |}{res}{col 25}-0.00546{col 37}     0.01{col 50}    1{col 64}0.9240
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.08930{col 37}     2.17{col 50}    1{col 64}0.1403
{txt}      milexp{col 19}{c |}{res}{col 25} 0.07044{col 37}     1.62{col 50}    1{col 64}0.2026
{txt}      female{col 19}{c |}{res}{col 25}-0.00295{col 37}     0.00{col 50}    1{col 64}0.9599
{txt}      agein{col 19}{c |}{res}{col 25} 0.02251{col 37}     0.21{col 50}    1{col 64}0.6506
{txt}      hog{col 19}{c |}{res}{col 25} 0.02624{col 37}     0.24{col 50}    1{col 64}0.6227
{txt}      education2{col 19}{c |}{res}{col 25} 0.00974{col 37}     0.04{col 50}    1{col 64}0.8413
{txt}      polity2{col 19}{c |}{res}{col 25} 0.09787{col 37}     3.81{col 50}    1{col 64}0.0508
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     6.80{col 50}    9{col 64}0.6580
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.01016{col 37}     0.03{col 50}    1{col 64}0.8537
{txt}      politics2{col 19}{c |}{res}{col 25} 0.05178{col 37}     0.82{col 50}    1{col 64}0.3654
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.11456{col 37}     3.58{col 50}    1{col 64}0.0585
{txt}      milexp{col 19}{c |}{res}{col 25} 0.05091{col 37}     0.85{col 50}    1{col 64}0.3571
{txt}      female{col 19}{c |}{res}{col 25} 0.00274{col 37}     0.00{col 50}    1{col 64}0.9627
{txt}      agein{col 19}{c |}{res}{col 25} 0.02894{col 37}     0.34{col 50}    1{col 64}0.5604
{txt}      hog{col 19}{c |}{res}{col 25} 0.00814{col 37}     0.02{col 50}    1{col 64}0.8787
{txt}      education2{col 19}{c |}{res}{col 25}-0.00709{col 37}     0.02{col 50}    1{col 64}0.8842
{txt}      polity2{col 19}{c |}{res}{col 25} 0.14503{col 37}     8.38{col 50}    1{col 64}0.0038
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    13.19{col 50}    9{col 64}0.1542
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.01421{col 37}     0.07{col 50}    1{col 64}0.7966
{txt}      politics2{col 19}{c |}{res}{col 25} 0.03037{col 37}     0.28{col 50}    1{col 64}0.5955
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.11771{col 37}     3.78{col 50}    1{col 64}0.0519
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08566{col 37}     2.40{col 50}    1{col 64}0.1213
{txt}      female{col 19}{c |}{res}{col 25}-0.00203{col 37}     0.00{col 50}    1{col 64}0.9723
{txt}      agein{col 19}{c |}{res}{col 25} 0.03247{col 37}     0.43{col 50}    1{col 64}0.5136
{txt}      hog{col 19}{c |}{res}{col 25} 0.00605{col 37}     0.01{col 50}    1{col 64}0.9097
{txt}      education2{col 19}{c |}{res}{col 25}-0.01252{col 37}     0.07{col 50}    1{col 64}0.7970
{txt}      polity2{col 19}{c |}{res}{col 25} 0.14277{col 37}     8.12{col 50}    1{col 64}0.0044
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    13.72{col 50}    9{col 64}0.1325
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.00943{col 37}     0.03{col 50}    1{col 64}0.8641
{txt}      politics2{col 19}{c |}{res}{col 25} 0.05745{col 37}     1.01{col 50}    1{col 64}0.3153
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12608{col 37}     4.34{col 50}    1{col 64}0.0373
{txt}      milexp{col 19}{c |}{res}{col 25} 0.07952{col 37}     2.07{col 50}    1{col 64}0.1503
{txt}      female{col 19}{c |}{res}{col 25} 0.00004{col 37}     0.00{col 50}    1{col 64}0.9995
{txt}      agein{col 19}{c |}{res}{col 25} 0.03808{col 37}     0.59{col 50}    1{col 64}0.4436
{txt}      hog{col 19}{c |}{res}{col 25}-0.00253{col 37}     0.00{col 50}    1{col 64}0.9622
{txt}      education2{col 19}{c |}{res}{col 25}-0.02368{col 37}     0.24{col 50}    1{col 64}0.6264
{txt}      polity2{col 19}{c |}{res}{col 25} 0.15941{col 37}    10.12{col 50}    1{col 64}0.0015
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    17.39{col 50}    9{col 64}0.0430
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************Model 2******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-724.72044{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-724.71854{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-724.71854{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-771.04586
{txt}Iteration 1:   log likelihood = {res}-728.67231
{txt}Iteration 2:   log likelihood = {res}-724.79936
{txt}Iteration 3:   log likelihood = {res}-724.71863
{txt}Iteration 4:   log likelihood = {res}-724.71854
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-724.71854

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       507
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         507{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         144{col 63}{txt}avg = {res} 36.214286
{txt}Time at risk    = {res}      524235{col 63}{txt}max = {res}        85

{col 49}{txt}Wald chi2({res}9{txt}){col 67}= {col 70}{res}    21.33
{txt}Log likelihood  =   {res}-724.71854{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0113

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.292948{col 27}{space 2} .2528884{col 38}{space 1}    1.31{col 47}{space 3}0.189{col 55}{space 4} .8812399{col 68}{space 3} 1.897003
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.223539{col 27}{space 2} .4767721{col 38}{space 1}    0.52{col 47}{space 3}0.605{col 55}{space 4} .5700767{col 68}{space 3} 2.626047
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .5341883{col 27}{space 2} .1120227{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .3541539{col 68}{space 3} .8057434
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.225091{col 27}{space 2} .2295301{col 38}{space 1}    1.08{col 47}{space 3}0.279{col 55}{space 4} .8485735{col 68}{space 3} 1.768673
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.374189{col 27}{space 2} 1.013465{col 38}{space 1}    0.43{col 47}{space 3}0.666{col 55}{space 4} .3238058{col 68}{space 3} 5.831876
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.020372{col 27}{space 2} .0123841{col 38}{space 1}    1.66{col 47}{space 3}0.097{col 55}{space 4} .9963862{col 68}{space 3} 1.044935
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .7333565{col 27}{space 2} .2071186{col 38}{space 1}   -1.10{col 47}{space 3}0.272{col 55}{space 4} .4216131{col 68}{space 3} 1.275605
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .8525156{col 27}{space 2} .0869636{col 38}{space 1}   -1.56{col 47}{space 3}0.118{col 55}{space 4} .6980279{col 68}{space 3} 1.041194
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9610964{col 27}{space 2} .0169962{col 38}{space 1}   -2.24{col 47}{space 3}0.025{col 55}{space 4}  .928355{col 68}{space 3} .9949924
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .8015923   .3750165
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}48.51{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.04540{col 37}     0.35{col 50}    1{col 64}0.5521
{txt}      politics2{col 19}{c |}{res}{col 25} 0.00056{col 37}     0.00{col 50}    1{col 64}0.9945
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.06217{col 37}     0.54{col 50}    1{col 64}0.4623
{txt}      milexp{col 19}{c |}{res}{col 25} 0.05569{col 37}     0.51{col 50}    1{col 64}0.4760
{txt}      female{col 19}{c |}{res}{col 25} 0.01132{col 37}     0.02{col 50}    1{col 64}0.8908
{txt}      agein{col 19}{c |}{res}{col 25} 0.01961{col 37}     0.08{col 50}    1{col 64}0.7815
{txt}      hog{col 19}{c |}{res}{col 25} 0.02849{col 37}     0.16{col 50}    1{col 64}0.6929
{txt}      education2{col 19}{c |}{res}{col 25}-0.00913{col 37}     0.02{col 50}    1{col 64}0.9023
{txt}      polity2{col 19}{c |}{res}{col 25} 0.05864{col 37}     0.90{col 50}    1{col 64}0.3422
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     2.07{col 50}    9{col 64}0.9902
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.01232{col 37}     0.03{col 50}    1{col 64}0.8718
{txt}      politics2{col 19}{c |}{res}{col 25} 0.11639{col 37}     2.05{col 50}    1{col 64}0.1523
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12587{col 37}     2.21{col 50}    1{col 64}0.1367
{txt}      milexp{col 19}{c |}{res}{col 25} 0.04802{col 37}     0.38{col 50}    1{col 64}0.5387
{txt}      female{col 19}{c |}{res}{col 25} 0.03049{col 37}     0.14{col 50}    1{col 64}0.7114
{txt}      agein{col 19}{c |}{res}{col 25} 0.02277{col 37}     0.10{col 50}    1{col 64}0.7474
{txt}      hog{col 19}{c |}{res}{col 25}-0.05082{col 37}     0.50{col 50}    1{col 64}0.4811
{txt}      education2{col 19}{c |}{res}{col 25}-0.12068{col 37}     2.63{col 50}    1{col 64}0.1046
{txt}      polity2{col 19}{c |}{res}{col 25} 0.11266{col 37}     3.33{col 50}    1{col 64}0.0680
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    10.78{col 50}    9{col 64}0.2908
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.05554{col 37}     0.53{col 50}    1{col 64}0.4670
{txt}      politics2{col 19}{c |}{res}{col 25} 0.02658{col 37}     0.11{col 50}    1{col 64}0.7438
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.05566{col 37}     0.43{col 50}    1{col 64}0.5105
{txt}      milexp{col 19}{c |}{res}{col 25} 0.06952{col 37}     0.79{col 50}    1{col 64}0.3735
{txt}      female{col 19}{c |}{res}{col 25} 0.01988{col 37}     0.06{col 50}    1{col 64}0.8094
{txt}      agein{col 19}{c |}{res}{col 25} 0.01866{col 37}     0.07{col 50}    1{col 64}0.7917
{txt}      hog{col 19}{c |}{res}{col 25}-0.04306{col 37}     0.36{col 50}    1{col 64}0.5506
{txt}      education2{col 19}{c |}{res}{col 25}-0.07972{col 37}     1.15{col 50}    1{col 64}0.2837
{txt}      polity2{col 19}{c |}{res}{col 25} 0.07670{col 37}     1.54{col 50}    1{col 64}0.2140
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     4.88{col 50}    9{col 64}0.8446
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.03987{col 37}     0.27{col 50}    1{col 64}0.6016
{txt}      politics2{col 19}{c |}{res}{col 25} 0.06558{col 37}     0.65{col 50}    1{col 64}0.4199
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.05505{col 37}     0.42{col 50}    1{col 64}0.5151
{txt}      milexp{col 19}{c |}{res}{col 25} 0.06852{col 37}     0.77{col 50}    1{col 64}0.3804
{txt}      female{col 19}{c |}{res}{col 25} 0.02742{col 37}     0.11{col 50}    1{col 64}0.7394
{txt}      agein{col 19}{c |}{res}{col 25} 0.02363{col 37}     0.11{col 50}    1{col 64}0.7381
{txt}      hog{col 19}{c |}{res}{col 25}-0.10255{col 37}     2.02{col 50}    1{col 64}0.1551
{txt}      education2{col 19}{c |}{res}{col 25}-0.13161{col 37}     3.13{col 50}    1{col 64}0.0767
{txt}      polity2{col 19}{c |}{res}{col 25} 0.08444{col 37}     1.87{col 50}    1{col 64}0.1713
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     9.81{col 50}    9{col 64}0.3660
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************Model 3******************
. stcox polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -1684.205{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1684.0486{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1684.0486{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -1835.858
{txt}Iteration 1:   log likelihood = {res}-1702.4489
{txt}Iteration 2:   log likelihood = {res}-1684.2642
{txt}Iteration 3:   log likelihood = {res}-1684.0487
{txt}Iteration 4:   log likelihood = {res}-1684.0486
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1684.0486

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       988
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         988{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         308{col 63}{txt}avg = {res} 70.571429
{txt}Time at risk    = {res}      761799{col 63}{txt}max = {res}       174

{col 49}{txt}Wald chi2({res}7{txt}){col 67}= {col 70}{res}   127.67
{txt}Log likelihood  =   {res}-1684.0486{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |} Haz. Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}polity2 {c |}{col 14}{res}{space 2} .9748873{col 26}{space 2} .0097051{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}   .95605{col 67}{space 3} .9940958
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} 1.270551{col 26}{space 2} .0641283{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} 1.150879{col 67}{space 3} 1.402668
{txt}{space 1}anyconflict {c |}{col 14}{res}{space 2} .8466107{col 26}{space 2} .2053462{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4} .5262887{col 67}{space 3} 1.361895
{txt}{space 9}win {c |}{col 14}{res}{space 2} .4292092{col 26}{space 2} .0911554{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4} .2830681{col 67}{space 3} .6507992
{txt}{space 8}lose {c |}{col 14}{res}{space 2} .7732673{col 26}{space 2} .1585792{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} .5173303{col 67}{space 3} 1.155823
{txt}{space 2}compromise {c |}{col 14}{res}{space 2} .6764576{col 26}{space 2} .1806541{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4} .4007924{col 67}{space 3} 1.141725
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2} .4067087{col 26}{space 2} .0974164{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4} .2543317{col 67}{space 3} .6503788
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       theta {c |}  {res} .4817929   .1951995
{txt}{hline 13}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}116.48{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.10967{col 37}     5.54{col 50}    1{col 64}0.0186
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02912{col 37}     0.29{col 50}    1{col 64}0.5934
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01101{col 37}     0.04{col 50}    1{col 64}0.8399
{txt}      win{col 19}{c |}{res}{col 25} 0.03868{col 37}     0.48{col 50}    1{col 64}0.4864
{txt}      lose{col 19}{c |}{res}{col 25} 0.06977{col 37}     1.58{col 50}    1{col 64}0.2083
{txt}      compromise{col 19}{c |}{res}{col 25} 0.01844{col 37}     0.12{col 50}    1{col 64}0.7322
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.02177{col 37}     0.15{col 50}    1{col 64}0.6986
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    13.87{col 50}    7{col 64}0.0535
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.14987{col 37}    10.34{col 50}    1{col 64}0.0013
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03908{col 37}     0.51{col 50}    1{col 64}0.4737
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01127{col 37}     0.04{col 50}    1{col 64}0.8362
{txt}      win{col 19}{c |}{res}{col 25}-0.00261{col 37}     0.00{col 50}    1{col 64}0.9625
{txt}      lose{col 19}{c |}{res}{col 25} 0.04840{col 37}     0.76{col 50}    1{col 64}0.3828
{txt}      compromise{col 19}{c |}{res}{col 25} 0.00030{col 37}     0.00{col 50}    1{col 64}0.9955
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.07590{col 37}     1.82{col 50}    1{col 64}0.1771
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    24.53{col 50}    7{col 64}0.0009
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.15097{col 37}    10.49{col 50}    1{col 64}0.0012
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03470{col 37}     0.40{col 50}    1{col 64}0.5246
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.02264{col 37}     0.17{col 50}    1{col 64}0.6778
{txt}      win{col 19}{c |}{res}{col 25} 0.02560{col 37}     0.21{col 50}    1{col 64}0.6451
{txt}      lose{col 19}{c |}{res}{col 25} 0.07322{col 37}     1.74{col 50}    1{col 64}0.1867
{txt}      compromise{col 19}{c |}{res}{col 25} 0.01665{col 37}     0.10{col 50}    1{col 64}0.7574
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.04494{col 37}     0.64{col 50}    1{col 64}0.4242
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    25.00{col 50}    7{col 64}0.0008
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.16137{col 37}    11.99{col 50}    1{col 64}0.0005
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02522{col 37}     0.21{col 50}    1{col 64}0.6437
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.03246{col 37}     0.35{col 50}    1{col 64}0.5515
{txt}      win{col 19}{c |}{res}{col 25}-0.00277{col 37}     0.00{col 50}    1{col 64}0.9603
{txt}      lose{col 19}{c |}{res}{col 25} 0.05506{col 37}     0.99{col 50}    1{col 64}0.3207
{txt}      compromise{col 19}{c |}{res}{col 25}-0.00740{col 37}     0.02{col 50}    1{col 64}0.8908
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.06408{col 37}     1.30{col 50}    1{col 64}0.2545
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    27.46{col 50}    7{col 64}0.0003
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************Model 4******************
. stcox polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-755.94743{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-755.94735{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-755.94734{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -847.8545
{txt}Iteration 1:   log likelihood = {res}-782.86694
{txt}Iteration 2:   log likelihood = {res}-756.62188
{txt}Iteration 3:   log likelihood = {res} -755.9502
{txt}Iteration 4:   log likelihood = {res}-755.94734
{txt}Iteration 5:   log likelihood = {res}-755.94734
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-755.94734

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       538
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         538{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         157{col 63}{txt}avg = {res} 38.428571
{txt}Time at risk    = {res}      551837{col 63}{txt}max = {res}        96

{col 49}{txt}Wald chi2({res}6{txt}){col 67}= {col 70}{res}   113.46
{txt}Log likelihood  =   {res}-755.94734{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |} Haz. Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}polity2 {c |}{col 14}{res}{space 2} .9859758{col 26}{space 2} .0142948{col 37}{space 1}   -0.97{col 46}{space 3}0.330{col 54}{space 4} .9583528{col 67}{space 3} 1.014395
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} 1.319913{col 26}{space 2} .0695482{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} 1.190404{col 67}{space 3} 1.463512
{txt}{space 9}win {c |}{col 14}{res}{space 2} .3498674{col 26}{space 2} .0771405{col 37}{space 1}   -4.76{col 46}{space 3}0.000{col 54}{space 4} .2271049{col 67}{space 3} .5389898
{txt}{space 8}lose {c |}{col 14}{res}{space 2} .6705535{col 26}{space 2} .1410679{col 37}{space 1}   -1.90{col 46}{space 3}0.057{col 54}{space 4} .4439782{col 67}{space 3} 1.012757
{txt}{space 2}compromise {c |}{col 14}{res}{space 2} .5774259{col 26}{space 2} .1570412{col 37}{space 1}   -2.02{col 46}{space 3}0.043{col 54}{space 4} .3388417{col 67}{space 3} .9840011
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2} .3394202{col 26}{space 2} .0849018{col 37}{space 1}   -4.32{col 46}{space 3}0.000{col 54}{space 4} .2078835{col 67}{space 3} .5541859
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       theta {c |}  {res} .7838236   .3748857
{txt}{hline 13}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}47.82{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.06446{col 37}     1.14{col 50}    1{col 64}0.2846
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03959{col 37}     0.28{col 50}    1{col 64}0.5970
{txt}      win{col 19}{c |}{res}{col 25} 0.03264{col 37}     0.18{col 50}    1{col 64}0.6684
{txt}      lose{col 19}{c |}{res}{col 25} 0.06887{col 37}     0.81{col 50}    1{col 64}0.3670
{txt}      compromise{col 19}{c |}{res}{col 25}-0.00281{col 37}     0.00{col 50}    1{col 64}0.9704
{txt}      Any_MID_End{col 19}{c |}{res}{col 25}-0.00452{col 37}     0.00{col 50}    1{col 64}0.9541
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     2.66{col 50}    6{col 64}0.8506
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.11606{col 37}     3.71{col 50}    1{col 64}0.0540
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03244{col 37}     0.19{col 50}    1{col 64}0.6648
{txt}      win{col 19}{c |}{res}{col 25} 0.00030{col 37}     0.00{col 50}    1{col 64}0.9968
{txt}      lose{col 19}{c |}{res}{col 25} 0.05817{col 37}     0.58{col 50}    1{col 64}0.4461
{txt}      compromise{col 19}{c |}{res}{col 25}-0.01736{col 37}     0.05{col 50}    1{col 64}0.8187
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.08181{col 37}     1.08{col 50}    1{col 64}0.2977
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     6.49{col 50}    6{col 64}0.3703
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.09344{col 37}     2.41{col 50}    1{col 64}0.1209
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.04878{col 37}     0.42{col 50}    1{col 64}0.5147
{txt}      win{col 19}{c |}{res}{col 25} 0.03239{col 37}     0.18{col 50}    1{col 64}0.6707
{txt}      lose{col 19}{c |}{res}{col 25} 0.07987{col 37}     1.09{col 50}    1{col 64}0.2955
{txt}      compromise{col 19}{c |}{res}{col 25}-0.00204{col 37}     0.00{col 50}    1{col 64}0.9786
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.01028{col 37}     0.02{col 50}    1{col 64}0.8959
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     4.55{col 50}    6{col 64}0.6028
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.11039{col 37}     3.36{col 50}    1{col 64}0.0669
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02860{col 37}     0.15{col 50}    1{col 64}0.7025
{txt}      win{col 19}{c |}{res}{col 25} 0.00556{col 37}     0.01{col 50}    1{col 64}0.9419
{txt}      lose{col 19}{c |}{res}{col 25} 0.06451{col 37}     0.71{col 50}    1{col 64}0.3982
{txt}      compromise{col 19}{c |}{res}{col 25}-0.03304{col 37}     0.19{col 50}    1{col 64}0.6628
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.03848{col 37}     0.24{col 50}    1{col 64}0.6242
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     5.02{col 50}    6{col 64}0.5416
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************Model 5******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1500.2752{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1500.1726{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1500.1725{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.6393
{txt}Iteration 1:   log likelihood = {res} -1519.031
{txt}Iteration 2:   log likelihood = {res}-1500.4473
{txt}Iteration 3:   log likelihood = {res}-1500.1727
{txt}Iteration 4:   log likelihood = {res}-1500.1725
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1500.1725

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}   143.73
{txt}Log likelihood  =   {res}-1500.1725{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.304525{col 27}{space 2} .1901437{col 38}{space 1}    1.82{col 47}{space 3}0.068{col 55}{space 4} .9803556{col 68}{space 3} 1.735885
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 2.355922{col 27}{space 2}  .677529{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} 1.340811{col 68}{space 3}  4.13956
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6760982{col 27}{space 2} .0934557{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4} .5156439{col 68}{space 3} .8864815
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.034429{col 27}{space 2} .1510073{col 38}{space 1}    0.23{col 47}{space 3}0.817{col 55}{space 4} .7770359{col 68}{space 3} 1.377083
{txt}{space 7}female {c |}{col 15}{res}{space 2}  1.13146{col 27}{space 2}  .527969{col 38}{space 1}    0.26{col 47}{space 3}0.791{col 55}{space 4}  .453363{col 68}{space 3} 2.823789
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.008962{col 27}{space 2} .0076522{col 38}{space 1}    1.18{col 47}{space 3}0.239{col 55}{space 4} .9940747{col 68}{space 3} 1.024072
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.142927{col 27}{space 2}  .218832{col 38}{space 1}    0.70{col 47}{space 3}0.485{col 55}{space 4} .7853119{col 68}{space 3} 1.663393
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  .822038{col 27}{space 2} .0591361{col 38}{space 1}   -2.72{col 47}{space 3}0.006{col 55}{space 4} .7139337{col 68}{space 3} .9465116
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9665297{col 27}{space 2} .0110131{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .9451837{col 68}{space 3} .9883577
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2}  1.32641{col 27}{space 2} .0724645{col 38}{space 1}    5.17{col 47}{space 3}0.000{col 55}{space 4} 1.191722{col 68}{space 3} 1.476321
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .7282609{col 27}{space 2}  .187134{col 38}{space 1}   -1.23{col 47}{space 3}0.217{col 55}{space 4}  .440111{col 68}{space 3} 1.205069
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4913114{col 27}{space 2}  .109074{col 38}{space 1}   -3.20{col 47}{space 3}0.001{col 55}{space 4} .3179694{col 68}{space 3} .7591516
{txt}{space 9}lose {c |}{col 15}{res}{space 2}  .768755{col 27}{space 2} .1656752{col 38}{space 1}   -1.22{col 47}{space 3}0.222{col 55}{space 4} .5039003{col 68}{space 3}  1.17282
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .5263131{col 27}{space 2} .1518892{col 38}{space 1}   -2.22{col 47}{space 3}0.026{col 55}{space 4} .2989477{col 68}{space 3}  .926602
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4142917{col 27}{space 2} .1042244{col 38}{space 1}   -3.50{col 47}{space 3}0.000{col 55}{space 4} .2530272{col 68}{space 3} .6783366
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res}   .50201   .2073325
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}93.30{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02312{col 37}     0.18{col 50}    1{col 64}0.6705
{txt}      politics2{col 19}{c |}{res}{col 25} 0.04354{col 37}     0.68{col 50}    1{col 64}0.4111
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12892{col 37}     4.49{col 50}    1{col 64}0.0341
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08421{col 37}     2.28{col 50}    1{col 64}0.1307
{txt}      female{col 19}{c |}{res}{col 25} 0.00942{col 37}     0.03{col 50}    1{col 64}0.8736
{txt}      agein{col 19}{c |}{res}{col 25} 0.02870{col 37}     0.32{col 50}    1{col 64}0.5708
{txt}      hog{col 19}{c |}{res}{col 25} 0.09013{col 37}     2.95{col 50}    1{col 64}0.0858
{txt}      education2{col 19}{c |}{res}{col 25} 0.00856{col 37}     0.03{col 50}    1{col 64}0.8577
{txt}      polity2{col 19}{c |}{res}{col 25} 0.09195{col 37}     3.31{col 50}    1{col 64}0.0690
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02111{col 37}     0.15{col 50}    1{col 64}0.6982
{txt}      anyconflict{col 19}{c |}{res}{col 25}-0.00084{col 37}     0.00{col 50}    1{col 64}0.9879
{txt}      win{col 19}{c |}{res}{col 25} 0.02288{col 37}     0.16{col 50}    1{col 64}0.6913
{txt}      lose{col 19}{c |}{res}{col 25} 0.02136{col 37}     0.14{col 50}    1{col 64}0.7082
{txt}      compromise{col 19}{c |}{res}{col 25}-0.01253{col 37}     0.05{col 50}    1{col 64}0.8232
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.01041{col 37}     0.03{col 50}    1{col 64}0.8573
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    15.15{col 50}   15{col 64}0.4407
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.00144{col 37}     0.00{col 50}    1{col 64}0.9789
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07347{col 37}     1.92{col 50}    1{col 64}0.1654
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12405{col 37}     4.16{col 50}    1{col 64}0.0415
{txt}      milexp{col 19}{c |}{res}{col 25} 0.04436{col 37}     0.63{col 50}    1{col 64}0.4259
{txt}      female{col 19}{c |}{res}{col 25} 0.01014{col 37}     0.03{col 50}    1{col 64}0.8641
{txt}      agein{col 19}{c |}{res}{col 25} 0.02973{col 37}     0.34{col 50}    1{col 64}0.5571
{txt}      hog{col 19}{c |}{res}{col 25} 0.08084{col 37}     2.37{col 50}    1{col 64}0.1233
{txt}      education2{col 19}{c |}{res}{col 25}-0.01807{col 37}     0.14{col 50}    1{col 64}0.7050
{txt}      polity2{col 19}{c |}{res}{col 25} 0.13207{col 37}     6.82{col 50}    1{col 64}0.0090
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02709{col 37}     0.25{col 50}    1{col 64}0.6188
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.00227{col 37}     0.00{col 50}    1{col 64}0.9671
{txt}      win{col 19}{c |}{res}{col 25}-0.01295{col 37}     0.05{col 50}    1{col 64}0.8221
{txt}      lose{col 19}{c |}{res}{col 25} 0.02546{col 37}     0.20{col 50}    1{col 64}0.6555
{txt}      compromise{col 19}{c |}{res}{col 25}-0.03346{col 37}     0.36{col 50}    1{col 64}0.5508
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.06395{col 37}     1.22{col 50}    1{col 64}0.2691
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    23.08{col 50}   15{col 64}0.0824
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02990{col 37}     0.30{col 50}    1{col 64}0.5822
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07390{col 37}     1.95{col 50}    1{col 64}0.1629
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.14497{col 37}     5.68{col 50}    1{col 64}0.0172
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08545{col 37}     2.35{col 50}    1{col 64}0.1251
{txt}      female{col 19}{c |}{res}{col 25} 0.01058{col 37}     0.03{col 50}    1{col 64}0.8581
{txt}      agein{col 19}{c |}{res}{col 25} 0.03644{col 37}     0.52{col 50}    1{col 64}0.4717
{txt}      hog{col 19}{c |}{res}{col 25} 0.08069{col 37}     2.37{col 50}    1{col 64}0.1240
{txt}      education2{col 19}{c |}{res}{col 25}-0.02062{col 37}     0.19{col 50}    1{col 64}0.6657
{txt}      polity2{col 19}{c |}{res}{col 25} 0.13116{col 37}     6.73{col 50}    1{col 64}0.0095
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02585{col 37}     0.23{col 50}    1{col 64}0.6350
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01301{col 37}     0.06{col 50}    1{col 64}0.8135
{txt}      win{col 19}{c |}{res}{col 25} 0.01134{col 37}     0.04{col 50}    1{col 64}0.8439
{txt}      lose{col 19}{c |}{res}{col 25} 0.02994{col 37}     0.28{col 50}    1{col 64}0.5998
{txt}      compromise{col 19}{c |}{res}{col 25}-0.02081{col 37}     0.14{col 50}    1{col 64}0.7106
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.02564{col 37}     0.20{col 50}    1{col 64}0.6576
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    24.72{col 50}   15{col 64}0.0538
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02336{col 37}     0.18{col 50}    1{col 64}0.6672
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07939{col 37}     2.25{col 50}    1{col 64}0.1339
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.13506{col 37}     4.93{col 50}    1{col 64}0.0265
{txt}      milexp{col 19}{c |}{res}{col 25} 0.06823{col 37}     1.50{col 50}    1{col 64}0.2207
{txt}      female{col 19}{c |}{res}{col 25} 0.00924{col 37}     0.02{col 50}    1{col 64}0.8759
{txt}      agein{col 19}{c |}{res}{col 25} 0.03730{col 37}     0.54{col 50}    1{col 64}0.4614
{txt}      hog{col 19}{c |}{res}{col 25} 0.07600{col 37}     2.10{col 50}    1{col 64}0.1474
{txt}      education2{col 19}{c |}{res}{col 25}-0.03682{col 37}     0.59{col 50}    1{col 64}0.4405
{txt}      polity2{col 19}{c |}{res}{col 25} 0.14683{col 37}     8.43{col 50}    1{col 64}0.0037
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.01540{col 37}     0.08{col 50}    1{col 64}0.7773
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.02874{col 37}     0.27{col 50}    1{col 64}0.6023
{txt}      win{col 19}{c |}{res}{col 25}-0.01255{col 37}     0.05{col 50}    1{col 64}0.8275
{txt}      lose{col 19}{c |}{res}{col 25} 0.02982{col 37}     0.27{col 50}    1{col 64}0.6013
{txt}      compromise{col 19}{c |}{res}{col 25}-0.04499{col 37}     0.64{col 50}    1{col 64}0.4224
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.04308{col 37}     0.55{col 50}    1{col 64}0.4565
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    28.27{col 50}   15{col 64}0.0199
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************Model 6******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev win lose compromise Any_MID_End if anyconflict==1, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -677.8903{txt}  
Iteration 1:{col 16}log profile likelihood = {res} -677.8903{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-772.15705
{txt}Iteration 1:   log likelihood = {res}-706.16466
{txt}Iteration 2:   log likelihood = {res} -678.7505
{txt}Iteration 3:   log likelihood = {res}-677.89127
{txt}Iteration 4:   log likelihood = {res} -677.8903
{txt}Refining estimates:
Iteration 0:   log likelihood = {res} -677.8903

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       507
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         507{col 63}{txt}min = {res}         6
{txt}No. of failures = {res}         144{col 63}{txt}avg = {res} 36.214286
{txt}Time at risk    = {res}      524235{col 63}{txt}max = {res}        85

{col 49}{txt}Wald chi2({res}14{txt}){col 67}= {col 70}{res}   114.03
{txt}Log likelihood  =   {res} -677.8903{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.291856{col 27}{space 2} .2564937{col 38}{space 1}    1.29{col 47}{space 3}0.197{col 55}{space 4} .8754091{col 68}{space 3} 1.906415
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.281336{col 27}{space 2} .5182397{col 38}{space 1}    0.61{col 47}{space 3}0.540{col 55}{space 4} .5799525{col 68}{space 3}  2.83096
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .5353217{col 27}{space 2}   .11276{col 38}{space 1}   -2.97{col 47}{space 3}0.003{col 55}{space 4} .3542566{col 68}{space 3} .8089315
{txt}{space 7}milexp {c |}{col 15}{res}{space 2}  1.23107{col 27}{space 2} .2341299{col 38}{space 1}    1.09{col 47}{space 3}0.274{col 55}{space 4} .8480042{col 68}{space 3} 1.787178
{txt}{space 7}female {c |}{col 15}{res}{space 2} 1.052681{col 27}{space 2} .7815771{col 38}{space 1}    0.07{col 47}{space 3}0.945{col 55}{space 4} .2456467{col 68}{space 3} 4.511103
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.010158{col 27}{space 2} .0124684{col 38}{space 1}    0.82{col 47}{space 3}0.413{col 55}{space 4} .9860135{col 68}{space 3} 1.034893
{txt}{space 10}hog {c |}{col 15}{res}{space 2} .7629564{col 27}{space 2} .2312819{col 38}{space 1}   -0.89{col 47}{space 3}0.372{col 55}{space 4} .4211795{col 68}{space 3} 1.382077
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .7720681{col 27}{space 2} .0818764{col 38}{space 1}   -2.44{col 47}{space 3}0.015{col 55}{space 4} .6271728{col 68}{space 3} .9504383
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9711065{col 27}{space 2} .0168002{col 38}{space 1}   -1.69{col 47}{space 3}0.090{col 55}{space 4} .9387307{col 68}{space 3} 1.004599
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.374319{col 27}{space 2} .0796658{col 38}{space 1}    5.49{col 47}{space 3}0.000{col 55}{space 4}  1.22672{col 68}{space 3} 1.539676
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4236912{col 27}{space 2} .0994014{col 38}{space 1}   -3.66{col 47}{space 3}0.000{col 55}{space 4} .2675166{col 68}{space 3} .6710397
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .6586043{col 27}{space 2} .1489797{col 38}{space 1}   -1.85{col 47}{space 3}0.065{col 55}{space 4} .4227448{col 68}{space 3} 1.026055
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .4894078{col 27}{space 2} .1492958{col 38}{space 1}   -2.34{col 47}{space 3}0.019{col 55}{space 4} .2691587{col 68}{space 3} .8898837
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .3301941{col 27}{space 2} .0878024{col 38}{space 1}   -4.17{col 47}{space 3}0.000{col 55}{space 4} .1960763{col 68}{space 3} .5560498
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .9693877   .4401124
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}50.77{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02915{col 37}     0.15{col 50}    1{col 64}0.6997
{txt}      politics2{col 19}{c |}{res}{col 25}-0.01408{col 37}     0.03{col 50}    1{col 64}0.8586
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.10947{col 37}     1.67{col 50}    1{col 64}0.1966
{txt}      milexp{col 19}{c |}{res}{col 25} 0.05402{col 37}     0.48{col 50}    1{col 64}0.4867
{txt}      female{col 19}{c |}{res}{col 25} 0.01458{col 37}     0.03{col 50}    1{col 64}0.8590
{txt}      agein{col 19}{c |}{res}{col 25} 0.00107{col 37}     0.00{col 50}    1{col 64}0.9876
{txt}      hog{col 19}{c |}{res}{col 25} 0.09956{col 37}     2.01{col 50}    1{col 64}0.1563
{txt}      education2{col 19}{c |}{res}{col 25}-0.01379{col 37}     0.04{col 50}    1{col 64}0.8488
{txt}      polity2{col 19}{c |}{res}{col 25} 0.04360{col 37}     0.50{col 50}    1{col 64}0.4780
{txt}      maxhostlev{col 19}{c |}{res}{col 25}-0.00382{col 37}     0.00{col 50}    1{col 64}0.9584
{txt}      win{col 19}{c |}{res}{col 25} 0.01690{col 37}     0.05{col 50}    1{col 64}0.8291
{txt}      lose{col 19}{c |}{res}{col 25}-0.00183{col 37}     0.00{col 50}    1{col 64}0.9809
{txt}      compromise{col 19}{c |}{res}{col 25}-0.04000{col 37}     0.27{col 50}    1{col 64}0.6066
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.01930{col 37}     0.06{col 50}    1{col 64}0.8091
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     4.06{col 50}   14{col 64}0.9951
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.02620{col 37}     0.12{col 50}    1{col 64}0.7289
{txt}      politics2{col 19}{c |}{res}{col 25} 0.10891{col 37}     1.90{col 50}    1{col 64}0.1681
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.18016{col 37}     4.52{col 50}    1{col 64}0.0335
{txt}      milexp{col 19}{c |}{res}{col 25} 0.02211{col 37}     0.08{col 50}    1{col 64}0.7759
{txt}      female{col 19}{c |}{res}{col 25} 0.02313{col 37}     0.08{col 50}    1{col 64}0.7781
{txt}      agein{col 19}{c |}{res}{col 25}-0.00124{col 37}     0.00{col 50}    1{col 64}0.9856
{txt}      hog{col 19}{c |}{res}{col 25} 0.05413{col 37}     0.59{col 50}    1{col 64}0.4408
{txt}      education2{col 19}{c |}{res}{col 25}-0.11226{col 37}     2.41{col 50}    1{col 64}0.1206
{txt}      polity2{col 19}{c |}{res}{col 25} 0.07026{col 37}     1.31{col 50}    1{col 64}0.2529
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00833{col 37}     0.01{col 50}    1{col 64}0.9095
{txt}      win{col 19}{c |}{res}{col 25}-0.02434{col 37}     0.10{col 50}    1{col 64}0.7558
{txt}      lose{col 19}{c |}{res}{col 25} 0.00490{col 37}     0.00{col 50}    1{col 64}0.9489
{txt}      compromise{col 19}{c |}{res}{col 25}-0.03219{col 37}     0.17{col 50}    1{col 64}0.6785
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.10806{col 37}     1.83{col 50}    1{col 64}0.1761
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    10.21{col 50}   14{col 64}0.7468
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.03474{col 37}     0.21{col 50}    1{col 64}0.6458
{txt}      politics2{col 19}{c |}{res}{col 25} 0.01464{col 37}     0.03{col 50}    1{col 64}0.8530
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.11048{col 37}     1.70{col 50}    1{col 64}0.1924
{txt}      milexp{col 19}{c |}{res}{col 25} 0.04725{col 37}     0.37{col 50}    1{col 64}0.5430
{txt}      female{col 19}{c |}{res}{col 25} 0.01851{col 37}     0.05{col 50}    1{col 64}0.8216
{txt}      agein{col 19}{c |}{res}{col 25}-0.00208{col 37}     0.00{col 50}    1{col 64}0.9759
{txt}      hog{col 19}{c |}{res}{col 25} 0.04826{col 37}     0.47{col 50}    1{col 64}0.4920
{txt}      education2{col 19}{c |}{res}{col 25}-0.07709{col 37}     1.14{col 50}    1{col 64}0.2865
{txt}      polity2{col 19}{c |}{res}{col 25} 0.04756{col 37}     0.60{col 50}    1{col 64}0.4389
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00823{col 37}     0.01{col 50}    1{col 64}0.9106
{txt}      win{col 19}{c |}{res}{col 25} 0.01436{col 37}     0.03{col 50}    1{col 64}0.8545
{txt}      lose{col 19}{c |}{res}{col 25}-0.00373{col 37}     0.00{col 50}    1{col 64}0.9611
{txt}      compromise{col 19}{c |}{res}{col 25}-0.02991{col 37}     0.15{col 50}    1{col 64}0.7002
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.03262{col 37}     0.17{col 50}    1{col 64}0.6830
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     4.20{col 50}   14{col 64}0.9941
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.01340{col 37}     0.03{col 50}    1{col 64}0.8593
{txt}      politics2{col 19}{c |}{res}{col 25} 0.05849{col 37}     0.55{col 50}    1{col 64}0.4592
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.11471{col 37}     1.83{col 50}    1{col 64}0.1760
{txt}      milexp{col 19}{c |}{res}{col 25} 0.02624{col 37}     0.11{col 50}    1{col 64}0.7355
{txt}      female{col 19}{c |}{res}{col 25} 0.01916{col 37}     0.05{col 50}    1{col 64}0.8155
{txt}      agein{col 19}{c |}{res}{col 25} 0.00162{col 37}     0.00{col 50}    1{col 64}0.9812
{txt}      hog{col 19}{c |}{res}{col 25} 0.00654{col 37}     0.01{col 50}    1{col 64}0.9258
{txt}      education2{col 19}{c |}{res}{col 25}-0.11848{col 37}     2.68{col 50}    1{col 64}0.1014
{txt}      polity2{col 19}{c |}{res}{col 25} 0.04512{col 37}     0.54{col 50}    1{col 64}0.4628
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00052{col 37}     0.00{col 50}    1{col 64}0.9944
{txt}      win{col 19}{c |}{res}{col 25}-0.01041{col 37}     0.02{col 50}    1{col 64}0.8942
{txt}      lose{col 19}{c |}{res}{col 25}-0.00603{col 37}     0.01{col 50}    1{col 64}0.9371
{txt}      compromise{col 19}{c |}{res}{col 25}-0.04374{col 37}     0.32{col 50}    1{col 64}0.5733
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.05622{col 37}     0.50{col 50}    1{col 64}0.4816
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     6.32{col 50}   14{col 64}0.9578
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. ******************************************
. ******************Table A2****************
. ******************************************
. 
. ******************Fix Model 3******************
. stcox polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -1684.205{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1684.0486{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1684.0486{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -1835.858
{txt}Iteration 1:   log likelihood = {res}-1702.4489
{txt}Iteration 2:   log likelihood = {res}-1684.2642
{txt}Iteration 3:   log likelihood = {res}-1684.0487
{txt}Iteration 4:   log likelihood = {res}-1684.0486
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1684.0486

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       988
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         988{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         308{col 63}{txt}avg = {res} 70.571429
{txt}Time at risk    = {res}      761799{col 63}{txt}max = {res}       174

{col 49}{txt}Wald chi2({res}7{txt}){col 67}= {col 70}{res}   127.67
{txt}Log likelihood  =   {res}-1684.0486{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |} Haz. Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}polity2 {c |}{col 14}{res}{space 2} .9748873{col 26}{space 2} .0097051{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}   .95605{col 67}{space 3} .9940958
{txt}{space 2}maxhostlev {c |}{col 14}{res}{space 2} 1.270551{col 26}{space 2} .0641283{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} 1.150879{col 67}{space 3} 1.402668
{txt}{space 1}anyconflict {c |}{col 14}{res}{space 2} .8466107{col 26}{space 2} .2053462{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4} .5262887{col 67}{space 3} 1.361895
{txt}{space 9}win {c |}{col 14}{res}{space 2} .4292092{col 26}{space 2} .0911554{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4} .2830681{col 67}{space 3} .6507992
{txt}{space 8}lose {c |}{col 14}{res}{space 2} .7732673{col 26}{space 2} .1585792{col 37}{space 1}   -1.25{col 46}{space 3}0.210{col 54}{space 4} .5173303{col 67}{space 3} 1.155823
{txt}{space 2}compromise {c |}{col 14}{res}{space 2} .6764576{col 26}{space 2} .1806541{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4} .4007924{col 67}{space 3} 1.141725
{txt}{space 1}Any_MID_End {c |}{col 14}{res}{space 2} .4067087{col 26}{space 2} .0974164{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4} .2543317{col 67}{space 3} .6503788
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       theta {c |}  {res} .4817929   .1951995
{txt}{hline 13}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}116.48{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.10967{col 37}     5.54{col 50}    1{col 64}0.0186
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02912{col 37}     0.29{col 50}    1{col 64}0.5934
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01101{col 37}     0.04{col 50}    1{col 64}0.8399
{txt}      win{col 19}{c |}{res}{col 25} 0.03868{col 37}     0.48{col 50}    1{col 64}0.4864
{txt}      lose{col 19}{c |}{res}{col 25} 0.06977{col 37}     1.58{col 50}    1{col 64}0.2083
{txt}      compromise{col 19}{c |}{res}{col 25} 0.01844{col 37}     0.12{col 50}    1{col 64}0.7322
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.02177{col 37}     0.15{col 50}    1{col 64}0.6986
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    13.87{col 50}    7{col 64}0.0535
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.14987{col 37}    10.34{col 50}    1{col 64}0.0013
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03908{col 37}     0.51{col 50}    1{col 64}0.4737
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01127{col 37}     0.04{col 50}    1{col 64}0.8362
{txt}      win{col 19}{c |}{res}{col 25}-0.00261{col 37}     0.00{col 50}    1{col 64}0.9625
{txt}      lose{col 19}{c |}{res}{col 25} 0.04840{col 37}     0.76{col 50}    1{col 64}0.3828
{txt}      compromise{col 19}{c |}{res}{col 25} 0.00030{col 37}     0.00{col 50}    1{col 64}0.9955
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.07590{col 37}     1.82{col 50}    1{col 64}0.1771
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    24.53{col 50}    7{col 64}0.0009
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.15097{col 37}    10.49{col 50}    1{col 64}0.0012
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.03470{col 37}     0.40{col 50}    1{col 64}0.5246
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.02264{col 37}     0.17{col 50}    1{col 64}0.6778
{txt}      win{col 19}{c |}{res}{col 25} 0.02560{col 37}     0.21{col 50}    1{col 64}0.6451
{txt}      lose{col 19}{c |}{res}{col 25} 0.07322{col 37}     1.74{col 50}    1{col 64}0.1867
{txt}      compromise{col 19}{c |}{res}{col 25} 0.01665{col 37}     0.10{col 50}    1{col 64}0.7574
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.04494{col 37}     0.64{col 50}    1{col 64}0.4242
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    25.00{col 50}    7{col 64}0.0008
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      polity2{col 19}{c |}{res}{col 25} 0.16137{col 37}    11.99{col 50}    1{col 64}0.0005
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02522{col 37}     0.21{col 50}    1{col 64}0.6437
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.03246{col 37}     0.35{col 50}    1{col 64}0.5515
{txt}      win{col 19}{c |}{res}{col 25}-0.00277{col 37}     0.00{col 50}    1{col 64}0.9603
{txt}      lose{col 19}{c |}{res}{col 25} 0.05506{col 37}     0.99{col 50}    1{col 64}0.3207
{txt}      compromise{col 19}{c |}{res}{col 25}-0.00740{col 37}     0.02{col 50}    1{col 64}0.8908
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.06408{col 37}     1.30{col 50}    1{col 64}0.2545
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    27.46{col 50}    7{col 64}0.0003
{txt}      {hline 12}{c BT}{hline 51}

{com}. *Polity2 is not proportional*
. sum tenure_month

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
tenure_month {c |}{res}      1,091    799.5875    1150.788          0      14482
{txt}
{com}. gen ln_tenure_month=ln(tenure_month+1)
{txt}(12 missing values generated)

{com}. gen lntime_polity2=ln_tenure_month*polity2
{txt}(111 missing values generated)

{com}. stcox polity2 lntime_polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -1680.157{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1680.0414{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1680.0406{txt}  
Iteration 3:{col 16}log profile likelihood = {res}-1680.0406{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1836.0626
{txt}Iteration 1:   log likelihood = {res} -1699.021
{txt}Iteration 2:   log likelihood = {res}-1680.2514
{txt}Iteration 3:   log likelihood = {res}-1680.0407
{txt}Iteration 4:   log likelihood = {res}-1680.0406
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1680.0406

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       988
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         988{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         308{col 63}{txt}avg = {res} 70.571429
{txt}Time at risk    = {res}      761799{col 63}{txt}max = {res}       174

{col 49}{txt}Wald chi2({res}8{txt}){col 67}= {col 70}{res}   136.64
{txt}Log likelihood  =   {res}-1680.0406{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            _t{col 16}{c |} Haz. Ratio{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}polity2 {c |}{col 16}{res}{space 2}  .833259{col 28}{space 2} .0469613{col 39}{space 1}   -3.24{col 48}{space 3}0.001{col 56}{space 4}  .746118{col 69}{space 3} .9305775
{txt}lntime_polity2 {c |}{col 16}{res}{space 2} 1.023183{col 28}{space 2} .0082634{col 39}{space 1}    2.84{col 48}{space 3}0.005{col 56}{space 4} 1.007115{col 69}{space 3} 1.039508
{txt}{space 4}maxhostlev {c |}{col 16}{res}{space 2} 1.257909{col 28}{space 2}  .063596{col 39}{space 1}    4.54{col 48}{space 3}0.000{col 56}{space 4}  1.13924{col 69}{space 3} 1.388939
{txt}{space 3}anyconflict {c |}{col 16}{res}{space 2} .8809056{col 28}{space 2}  .213916{col 39}{space 1}   -0.52{col 48}{space 3}0.602{col 56}{space 4} .5473014{col 69}{space 3} 1.417856
{txt}{space 11}win {c |}{col 16}{res}{space 2} .4357491{col 28}{space 2}  .092283{col 39}{space 1}   -3.92{col 48}{space 3}0.000{col 56}{space 4} .2877193{col 69}{space 3} .6599394
{txt}{space 10}lose {c |}{col 16}{res}{space 2} .7735286{col 28}{space 2} .1584531{col 39}{space 1}   -1.25{col 48}{space 3}0.210{col 56}{space 4} .5177408{col 69}{space 3} 1.155687
{txt}{space 4}compromise {c |}{col 16}{res}{space 2} .6756783{col 28}{space 2} .1796829{col 39}{space 1}   -1.47{col 48}{space 3}0.140{col 56}{space 4} .4012178{col 69}{space 3} 1.137889
{txt}{space 3}Any_MID_End {c |}{col 16}{res}{space 2}  .390726{col 28}{space 2} .0937484{col 39}{space 1}   -3.92{col 48}{space 3}0.000{col 56}{space 4} .2441407{col 69}{space 3} .6253228
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         theta {c |}  {res} .4975444   .2015577
{txt}{hline 15}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}118.23{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. 
. ******************Fix Model 5******************
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1500.2752{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1500.1726{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1500.1725{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.6393
{txt}Iteration 1:   log likelihood = {res} -1519.031
{txt}Iteration 2:   log likelihood = {res}-1500.4473
{txt}Iteration 3:   log likelihood = {res}-1500.1727
{txt}Iteration 4:   log likelihood = {res}-1500.1725
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1500.1725

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}   143.73
{txt}Log likelihood  =   {res}-1500.1725{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.304525{col 27}{space 2} .1901437{col 38}{space 1}    1.82{col 47}{space 3}0.068{col 55}{space 4} .9803556{col 68}{space 3} 1.735885
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 2.355922{col 27}{space 2}  .677529{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} 1.340811{col 68}{space 3}  4.13956
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6760982{col 27}{space 2} .0934557{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4} .5156439{col 68}{space 3} .8864815
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.034429{col 27}{space 2} .1510073{col 38}{space 1}    0.23{col 47}{space 3}0.817{col 55}{space 4} .7770359{col 68}{space 3} 1.377083
{txt}{space 7}female {c |}{col 15}{res}{space 2}  1.13146{col 27}{space 2}  .527969{col 38}{space 1}    0.26{col 47}{space 3}0.791{col 55}{space 4}  .453363{col 68}{space 3} 2.823789
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.008962{col 27}{space 2} .0076522{col 38}{space 1}    1.18{col 47}{space 3}0.239{col 55}{space 4} .9940747{col 68}{space 3} 1.024072
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.142927{col 27}{space 2}  .218832{col 38}{space 1}    0.70{col 47}{space 3}0.485{col 55}{space 4} .7853119{col 68}{space 3} 1.663393
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  .822038{col 27}{space 2} .0591361{col 38}{space 1}   -2.72{col 47}{space 3}0.006{col 55}{space 4} .7139337{col 68}{space 3} .9465116
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9665297{col 27}{space 2} .0110131{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .9451837{col 68}{space 3} .9883577
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2}  1.32641{col 27}{space 2} .0724645{col 38}{space 1}    5.17{col 47}{space 3}0.000{col 55}{space 4} 1.191722{col 68}{space 3} 1.476321
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .7282609{col 27}{space 2}  .187134{col 38}{space 1}   -1.23{col 47}{space 3}0.217{col 55}{space 4}  .440111{col 68}{space 3} 1.205069
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4913114{col 27}{space 2}  .109074{col 38}{space 1}   -3.20{col 47}{space 3}0.001{col 55}{space 4} .3179694{col 68}{space 3} .7591516
{txt}{space 9}lose {c |}{col 15}{res}{space 2}  .768755{col 27}{space 2} .1656752{col 38}{space 1}   -1.22{col 47}{space 3}0.222{col 55}{space 4} .5039003{col 68}{space 3}  1.17282
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .5263131{col 27}{space 2} .1518892{col 38}{space 1}   -2.22{col 47}{space 3}0.026{col 55}{space 4} .2989477{col 68}{space 3}  .926602
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4142917{col 27}{space 2} .1042244{col 38}{space 1}   -3.50{col 47}{space 3}0.000{col 55}{space 4} .2530272{col 68}{space 3} .6783366
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res}   .50201   .2073325
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}93.30{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02312{col 37}     0.18{col 50}    1{col 64}0.6705
{txt}      politics2{col 19}{c |}{res}{col 25} 0.04354{col 37}     0.68{col 50}    1{col 64}0.4111
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12892{col 37}     4.49{col 50}    1{col 64}0.0341
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08421{col 37}     2.28{col 50}    1{col 64}0.1307
{txt}      female{col 19}{c |}{res}{col 25} 0.00942{col 37}     0.03{col 50}    1{col 64}0.8736
{txt}      agein{col 19}{c |}{res}{col 25} 0.02870{col 37}     0.32{col 50}    1{col 64}0.5708
{txt}      hog{col 19}{c |}{res}{col 25} 0.09013{col 37}     2.95{col 50}    1{col 64}0.0858
{txt}      education2{col 19}{c |}{res}{col 25} 0.00856{col 37}     0.03{col 50}    1{col 64}0.8577
{txt}      polity2{col 19}{c |}{res}{col 25} 0.09195{col 37}     3.31{col 50}    1{col 64}0.0690
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02111{col 37}     0.15{col 50}    1{col 64}0.6982
{txt}      anyconflict{col 19}{c |}{res}{col 25}-0.00084{col 37}     0.00{col 50}    1{col 64}0.9879
{txt}      win{col 19}{c |}{res}{col 25} 0.02288{col 37}     0.16{col 50}    1{col 64}0.6913
{txt}      lose{col 19}{c |}{res}{col 25} 0.02136{col 37}     0.14{col 50}    1{col 64}0.7082
{txt}      compromise{col 19}{c |}{res}{col 25}-0.01253{col 37}     0.05{col 50}    1{col 64}0.8232
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.01041{col 37}     0.03{col 50}    1{col 64}0.8573
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    15.15{col 50}   15{col 64}0.4407
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.00144{col 37}     0.00{col 50}    1{col 64}0.9789
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07347{col 37}     1.92{col 50}    1{col 64}0.1654
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.12405{col 37}     4.16{col 50}    1{col 64}0.0415
{txt}      milexp{col 19}{c |}{res}{col 25} 0.04436{col 37}     0.63{col 50}    1{col 64}0.4259
{txt}      female{col 19}{c |}{res}{col 25} 0.01014{col 37}     0.03{col 50}    1{col 64}0.8641
{txt}      agein{col 19}{c |}{res}{col 25} 0.02973{col 37}     0.34{col 50}    1{col 64}0.5571
{txt}      hog{col 19}{c |}{res}{col 25} 0.08084{col 37}     2.37{col 50}    1{col 64}0.1233
{txt}      education2{col 19}{c |}{res}{col 25}-0.01807{col 37}     0.14{col 50}    1{col 64}0.7050
{txt}      polity2{col 19}{c |}{res}{col 25} 0.13207{col 37}     6.82{col 50}    1{col 64}0.0090
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02709{col 37}     0.25{col 50}    1{col 64}0.6188
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.00227{col 37}     0.00{col 50}    1{col 64}0.9671
{txt}      win{col 19}{c |}{res}{col 25}-0.01295{col 37}     0.05{col 50}    1{col 64}0.8221
{txt}      lose{col 19}{c |}{res}{col 25} 0.02546{col 37}     0.20{col 50}    1{col 64}0.6555
{txt}      compromise{col 19}{c |}{res}{col 25}-0.03346{col 37}     0.36{col 50}    1{col 64}0.5508
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.06395{col 37}     1.22{col 50}    1{col 64}0.2691
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    23.08{col 50}   15{col 64}0.0824
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02990{col 37}     0.30{col 50}    1{col 64}0.5822
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07390{col 37}     1.95{col 50}    1{col 64}0.1629
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.14497{col 37}     5.68{col 50}    1{col 64}0.0172
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08545{col 37}     2.35{col 50}    1{col 64}0.1251
{txt}      female{col 19}{c |}{res}{col 25} 0.01058{col 37}     0.03{col 50}    1{col 64}0.8581
{txt}      agein{col 19}{c |}{res}{col 25} 0.03644{col 37}     0.52{col 50}    1{col 64}0.4717
{txt}      hog{col 19}{c |}{res}{col 25} 0.08069{col 37}     2.37{col 50}    1{col 64}0.1240
{txt}      education2{col 19}{c |}{res}{col 25}-0.02062{col 37}     0.19{col 50}    1{col 64}0.6657
{txt}      polity2{col 19}{c |}{res}{col 25} 0.13116{col 37}     6.73{col 50}    1{col 64}0.0095
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.02585{col 37}     0.23{col 50}    1{col 64}0.6350
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01301{col 37}     0.06{col 50}    1{col 64}0.8135
{txt}      win{col 19}{c |}{res}{col 25} 0.01134{col 37}     0.04{col 50}    1{col 64}0.8439
{txt}      lose{col 19}{c |}{res}{col 25} 0.02994{col 37}     0.28{col 50}    1{col 64}0.5998
{txt}      compromise{col 19}{c |}{res}{col 25}-0.02081{col 37}     0.14{col 50}    1{col 64}0.7106
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.02564{col 37}     0.20{col 50}    1{col 64}0.6576
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    24.72{col 50}   15{col 64}0.0538
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25} 0.02336{col 37}     0.18{col 50}    1{col 64}0.6672
{txt}      politics2{col 19}{c |}{res}{col 25} 0.07939{col 37}     2.25{col 50}    1{col 64}0.1339
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.13506{col 37}     4.93{col 50}    1{col 64}0.0265
{txt}      milexp{col 19}{c |}{res}{col 25} 0.06823{col 37}     1.50{col 50}    1{col 64}0.2207
{txt}      female{col 19}{c |}{res}{col 25} 0.00924{col 37}     0.02{col 50}    1{col 64}0.8759
{txt}      agein{col 19}{c |}{res}{col 25} 0.03730{col 37}     0.54{col 50}    1{col 64}0.4614
{txt}      hog{col 19}{c |}{res}{col 25} 0.07600{col 37}     2.10{col 50}    1{col 64}0.1474
{txt}      education2{col 19}{c |}{res}{col 25}-0.03682{col 37}     0.59{col 50}    1{col 64}0.4405
{txt}      polity2{col 19}{c |}{res}{col 25} 0.14683{col 37}     8.43{col 50}    1{col 64}0.0037
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.01540{col 37}     0.08{col 50}    1{col 64}0.7773
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.02874{col 37}     0.27{col 50}    1{col 64}0.6023
{txt}      win{col 19}{c |}{res}{col 25}-0.01255{col 37}     0.05{col 50}    1{col 64}0.8275
{txt}      lose{col 19}{c |}{res}{col 25} 0.02982{col 37}     0.27{col 50}    1{col 64}0.6013
{txt}      compromise{col 19}{c |}{res}{col 25}-0.04499{col 37}     0.64{col 50}    1{col 64}0.4224
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.04308{col 37}     0.55{col 50}    1{col 64}0.4565
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    28.27{col 50}   15{col 64}0.0199
{txt}      {hline 12}{c BT}{hline 51}

{com}. *Polity2 is strongly not proportional. Polity2 has been fixed above*
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 lntime_polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1499.4534{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1499.3761{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1499.3761{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res} -1659.802
{txt}Iteration 1:   log likelihood = {res}-1519.0459
{txt}Iteration 2:   log likelihood = {res}-1499.6644
{txt}Iteration 3:   log likelihood = {res}-1499.3763
{txt}Iteration 4:   log likelihood = {res}-1499.3761
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1499.3761

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}16{txt}){col 67}= {col 70}{res}   146.57
{txt}Log likelihood  =   {res}-1499.3761{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            _t{col 16}{c |} Haz. Ratio{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}previous_term {c |}{col 16}{res}{space 2} 1.288483{col 28}{space 2} .1879362{col 39}{space 1}    1.74{col 48}{space 3}0.082{col 56}{space 4} .9681078{col 69}{space 3}  1.71488
{txt}{space 5}politics2 {c |}{col 16}{res}{space 2} 2.297795{col 28}{space 2} .6609282{col 39}{space 1}    2.89{col 48}{space 3}0.004{col 56}{space 4} 1.307601{col 69}{space 3} 4.037823
{txt}{space 8}dipexp {c |}{col 16}{res}{space 2} .6740187{col 28}{space 2} .0930355{col 39}{space 1}   -2.86{col 48}{space 3}0.004{col 56}{space 4} .5142564{col 69}{space 3} .8834139
{txt}{space 8}milexp {c |}{col 16}{res}{space 2} 1.029266{col 28}{space 2} .1507078{col 39}{space 1}    0.20{col 48}{space 3}0.844{col 56}{space 4} .7724893{col 69}{space 3} 1.371396
{txt}{space 8}female {c |}{col 16}{res}{space 2}  1.15923{col 28}{space 2} .5417652{col 39}{space 1}    0.32{col 48}{space 3}0.752{col 56}{space 4} .4638325{col 69}{space 3} 2.897197
{txt}{space 9}agein {c |}{col 16}{res}{space 2} 1.008891{col 28}{space 2}  .007644{col 39}{space 1}    1.17{col 48}{space 3}0.243{col 56}{space 4} .9940193{col 69}{space 3} 1.023984
{txt}{space 11}hog {c |}{col 16}{res}{space 2} 1.139765{col 28}{space 2} .2184305{col 39}{space 1}    0.68{col 48}{space 3}0.495{col 56}{space 4} .7828649{col 69}{space 3} 1.659373
{txt}{space 4}education2 {c |}{col 16}{res}{space 2} .8257633{col 28}{space 2} .0595202{col 39}{space 1}   -2.66{col 48}{space 3}0.008{col 56}{space 4} .7169714{col 69}{space 3} .9510631
{txt}{space 7}polity2 {c |}{col 16}{res}{space 2} .8971038{col 28}{space 2} .0538633{col 39}{space 1}   -1.81{col 48}{space 3}0.071{col 56}{space 4} .7975087{col 69}{space 3} 1.009137
{txt}lntime_polity2 {c |}{col 16}{res}{space 2}  1.01099{col 28}{space 2} .0087307{col 39}{space 1}    1.27{col 48}{space 3}0.206{col 56}{space 4} .9940225{col 69}{space 3} 1.028248
{txt}{space 4}maxhostlev {c |}{col 16}{res}{space 2} 1.319162{col 28}{space 2} .0722365{col 39}{space 1}    5.06{col 48}{space 3}0.000{col 56}{space 4} 1.184914{col 69}{space 3} 1.468619
{txt}{space 3}anyconflict {c |}{col 16}{res}{space 2} .7426848{col 28}{space 2} .1909842{col 39}{space 1}   -1.16{col 48}{space 3}0.247{col 56}{space 4} .4486573{col 69}{space 3} 1.229403
{txt}{space 11}win {c |}{col 16}{res}{space 2}  .491694{col 28}{space 2} .1089744{col 39}{space 1}   -3.20{col 48}{space 3}0.001{col 56}{space 4}  .318451{col 69}{space 3} .7591841
{txt}{space 10}lose {c |}{col 16}{res}{space 2} .7751569{col 28}{space 2} .1672168{col 39}{space 1}   -1.18{col 48}{space 3}0.238{col 56}{space 4} .5078886{col 69}{space 3} 1.183071
{txt}{space 4}compromise {c |}{col 16}{res}{space 2} .5345944{col 28}{space 2} .1536301{col 39}{space 1}   -2.18{col 48}{space 3}0.029{col 56}{space 4} .3043748{col 69}{space 3} .9389449
{txt}{space 3}Any_MID_End {c |}{col 16}{res}{space 2} .4040748{col 28}{space 2} .1019653{col 39}{space 1}   -3.59{col 48}{space 3}0.000{col 56}{space 4} .2464151{col 69}{space 3} .6626073
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         theta {c |}  {res} .5149976   .2124372
{txt}{hline 15}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}94.72{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. 
. 
. ******************************************
. ******************Appendix 4**************
. ******************************************
. 
. ******************************************
. ******************Table A3****************
. ******************************************
. 
. *************Forced. Model 5 in Table 3****
. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1500.2752{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1500.1726{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1500.1725{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1659.6393
{txt}Iteration 1:   log likelihood = {res} -1519.031
{txt}Iteration 2:   log likelihood = {res}-1500.4473
{txt}Iteration 3:   log likelihood = {res}-1500.1727
{txt}Iteration 4:   log likelihood = {res}-1500.1725
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1500.1725

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         282{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}   143.73
{txt}Log likelihood  =   {res}-1500.1725{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.304525{col 27}{space 2} .1901437{col 38}{space 1}    1.82{col 47}{space 3}0.068{col 55}{space 4} .9803556{col 68}{space 3} 1.735885
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 2.355922{col 27}{space 2}  .677529{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} 1.340811{col 68}{space 3}  4.13956
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .6760982{col 27}{space 2} .0934557{col 38}{space 1}   -2.83{col 47}{space 3}0.005{col 55}{space 4} .5156439{col 68}{space 3} .8864815
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.034429{col 27}{space 2} .1510073{col 38}{space 1}    0.23{col 47}{space 3}0.817{col 55}{space 4} .7770359{col 68}{space 3} 1.377083
{txt}{space 7}female {c |}{col 15}{res}{space 2}  1.13146{col 27}{space 2}  .527969{col 38}{space 1}    0.26{col 47}{space 3}0.791{col 55}{space 4}  .453363{col 68}{space 3} 2.823789
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.008962{col 27}{space 2} .0076522{col 38}{space 1}    1.18{col 47}{space 3}0.239{col 55}{space 4} .9940747{col 68}{space 3} 1.024072
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.142927{col 27}{space 2}  .218832{col 38}{space 1}    0.70{col 47}{space 3}0.485{col 55}{space 4} .7853119{col 68}{space 3} 1.663393
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  .822038{col 27}{space 2} .0591361{col 38}{space 1}   -2.72{col 47}{space 3}0.006{col 55}{space 4} .7139337{col 68}{space 3} .9465116
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9665297{col 27}{space 2} .0110131{col 38}{space 1}   -2.99{col 47}{space 3}0.003{col 55}{space 4} .9451837{col 68}{space 3} .9883577
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2}  1.32641{col 27}{space 2} .0724645{col 38}{space 1}    5.17{col 47}{space 3}0.000{col 55}{space 4} 1.191722{col 68}{space 3} 1.476321
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .7282609{col 27}{space 2}  .187134{col 38}{space 1}   -1.23{col 47}{space 3}0.217{col 55}{space 4}  .440111{col 68}{space 3} 1.205069
{txt}{space 10}win {c |}{col 15}{res}{space 2} .4913114{col 27}{space 2}  .109074{col 38}{space 1}   -3.20{col 47}{space 3}0.001{col 55}{space 4} .3179694{col 68}{space 3} .7591516
{txt}{space 9}lose {c |}{col 15}{res}{space 2}  .768755{col 27}{space 2} .1656752{col 38}{space 1}   -1.22{col 47}{space 3}0.222{col 55}{space 4} .5039003{col 68}{space 3}  1.17282
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .5263131{col 27}{space 2} .1518892{col 38}{space 1}   -2.22{col 47}{space 3}0.026{col 55}{space 4} .2989477{col 68}{space 3}  .926602
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4142917{col 27}{space 2} .1042244{col 38}{space 1}   -3.50{col 47}{space 3}0.000{col 55}{space 4} .2530272{col 68}{space 3} .6783366
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res}   .50201   .2073325
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}93.30{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. stset,clear
{txt}
{com}. 
. ******************Retirement***************
. stset tenure_month, failure(retire==1)

     {txt}failure event:  {res}retire == 1
{txt}obs. time interval:  {res}(0, tenure_month]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}      1,103{txt}  total observations
{res}         12{txt}  event time missing (tenure_month>=.)               PROBABLE ERROR
{res}          4{txt}  observations end on or before enter()
{hline 78}
{res}      1,087{txt}  observations remaining, representing
{res}        195{txt}  failures in single-record/single-failure data
{res}    872,350{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   14,482
{txt}
{com}. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}retire == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -824.5845{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-824.42638{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-824.42635{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-878.91208
{txt}Iteration 1:   log likelihood = {res}-828.45125
{txt}Iteration 2:   log likelihood = {res}-824.46204
{txt}Iteration 3:   log likelihood = {res}-824.42636
{txt}Iteration 4:   log likelihood = {res}-824.42635
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-824.42635

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         148{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}    75.43
{txt}Log likelihood  =   {res}-824.42635{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} .8331194{col 27}{space 2} .1901833{col 38}{space 1}   -0.80{col 47}{space 3}0.424{col 55}{space 4} .5325939{col 68}{space 3} 1.303222
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} .9968129{col 27}{space 2} .3096548{col 38}{space 1}   -0.01{col 47}{space 3}0.992{col 55}{space 4}  .542241{col 68}{space 3} 1.832462
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .8864223{col 27}{space 2} .1727425{col 38}{space 1}   -0.62{col 47}{space 3}0.536{col 55}{space 4} .6050091{col 68}{space 3} 1.298732
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.382861{col 27}{space 2} .2586849{col 38}{space 1}    1.73{col 47}{space 3}0.083{col 55}{space 4} .9584035{col 68}{space 3} 1.995301
{txt}{space 7}female {c |}{col 15}{res}{space 2} .8987635{col 27}{space 2} .5403253{col 38}{space 1}   -0.18{col 47}{space 3}0.859{col 55}{space 4} .2766392{col 68}{space 3} 2.919961
{txt}{space 8}agein {c |}{col 15}{res}{space 2} .9979985{col 27}{space 2} .0102304{col 38}{space 1}   -0.20{col 47}{space 3}0.845{col 55}{space 4} .9781474{col 68}{space 3} 1.018253
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.944045{col 27}{space 2} .4703209{col 38}{space 1}    2.75{col 47}{space 3}0.006{col 55}{space 4} 1.209974{col 68}{space 3} 3.123466
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .9813166{col 27}{space 2}  .095296{col 38}{space 1}   -0.19{col 47}{space 3}0.846{col 55}{space 4} .8112387{col 68}{space 3} 1.187052
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9873724{col 27}{space 2} .0153719{col 38}{space 1}   -0.82{col 47}{space 3}0.414{col 55}{space 4}  .957699{col 68}{space 3} 1.017965
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.332548{col 27}{space 2} .0986566{col 38}{space 1}    3.88{col 47}{space 3}0.000{col 55}{space 4}  1.15256{col 68}{space 3} 1.540645
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .6282388{col 27}{space 2} .2297606{col 38}{space 1}   -1.27{col 47}{space 3}0.204{col 55}{space 4} .3067763{col 68}{space 3} 1.286553
{txt}{space 10}win {c |}{col 15}{res}{space 2} .8057276{col 27}{space 2} .2313249{col 38}{space 1}   -0.75{col 47}{space 3}0.452{col 55}{space 4} .4589948{col 68}{space 3} 1.414389
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .5839242{col 27}{space 2} .1766886{col 38}{space 1}   -1.78{col 47}{space 3}0.075{col 55}{space 4} .3226955{col 68}{space 3} 1.056623
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .4185745{col 27}{space 2} .1622119{col 38}{space 1}   -2.25{col 47}{space 3}0.025{col 55}{space 4} .1958407{col 68}{space 3} .8946279
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4142893{col 27}{space 2} .1442842{col 38}{space 1}   -2.53{col 47}{space 3}0.011{col 55}{space 4}  .209342{col 68}{space 3} .8198815
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .2690463   .1529502
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}12.30{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. stset,clear
{txt}
{com}. 
. ******************Violent******************
. stset tenure_month, failure(violent==1)

     {txt}failure event:  {res}violent == 1
{txt}obs. time interval:  {res}(0, tenure_month]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}      1,103{txt}  total observations
{res}         12{txt}  event time missing (tenure_month>=.)               PROBABLE ERROR
{res}          4{txt}  observations end on or before enter()
{hline 78}
{res}      1,087{txt}  observations remaining, representing
{res}         35{txt}  failures in single-record/single-failure data
{res}    872,350{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   14,482
{txt}
{com}. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}violent == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res} -138.1512{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-138.10814{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-138.10812{txt}  
Iteration 3:{col 16}log profile likelihood = {res}-138.10812{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-169.61795
{txt}Iteration 1:   log likelihood = {res}-149.53044
{txt}Iteration 2:   log likelihood = {res}-139.85287
{txt}Iteration 3:   log likelihood = {res}-138.26549
{txt}Iteration 4:   log likelihood = {res}-138.15016
{txt}Iteration 5:   log likelihood = {res} -138.1235
{txt}Iteration 6:   log likelihood = {res}-138.11377
{txt}Iteration 7:   log likelihood = {res} -138.1102
{txt}Iteration 8:   log likelihood = {res}-138.10888
{txt}Iteration 9:   log likelihood = {res} -138.1084
{txt}Iteration 10:  log likelihood = {res}-138.10822
{txt}Iteration 11:  log likelihood = {res}-138.10816
{txt}Iteration 12:  log likelihood = {res}-138.10813
{txt}Iteration 13:  log likelihood = {res}-138.10812
{txt}Iteration 14:  log likelihood = {res}-138.10812
{txt}Iteration 15:  log likelihood = {res}-138.10812
{txt}Iteration 16:  log likelihood = {res}-138.10812
{txt}Iteration 17:  log likelihood = {res}-138.10812
{txt}Iteration 18:  log likelihood = {res}-138.10812
{txt}Iteration 19:  log likelihood = {res}-138.10812
{txt}Iteration 20:  log likelihood = {res}-138.10812
{txt}Iteration 21:  log likelihood = {res}-138.10812
{txt}Iteration 22:  log likelihood = {res}-138.10812
{txt}Iteration 23:  log likelihood = {res}-138.10812
{txt}Iteration 24:  log likelihood = {res}-138.10812
{txt}Iteration 25:  log likelihood = {res}-138.10812
{txt}Iteration 26:  log likelihood = {res}-138.10812
{txt}Iteration 27:  log likelihood = {res}-138.10812
{txt}Iteration 28:  log likelihood = {res}-138.10812
{txt}Iteration 29:  log likelihood = {res}-138.10812
{txt}Iteration 30:  log likelihood = {res}-138.10812
{txt}Iteration 31:  log likelihood = {res}-138.10812
{txt}Iteration 32:  log likelihood = {res}-138.10812
{txt}Iteration 33:  log likelihood = {res}-138.10812
{txt}Iteration 34:  log likelihood = {res}-138.10812
{txt}Iteration 35:  log likelihood = {res}-138.10812
{txt}Iteration 36:  log likelihood = {res}-138.10812
{txt}Iteration 37:  log likelihood = {res}-138.10812
{txt}Iteration 38:  log likelihood = {res}-138.10812
{txt}Iteration 39:  log likelihood = {res}-138.10812
{txt}Iteration 40:  log likelihood = {res}-138.10812
{txt}Iteration 41:  log likelihood = {res}-138.10812
{txt}Iteration 42:  log likelihood = {res}-138.10812
{txt}Iteration 43:  log likelihood = {res}-138.10812
{txt}Iteration 44:  log likelihood = {res}-138.10812
{txt}Iteration 45:  log likelihood = {res}-138.10812
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-138.10812

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}          26{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}14{txt}){col 67}= {col 70}{res}    19.81
{txt}Log likelihood  =   {res}-138.10812{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.1361

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.484753{col 27}{space 2} .6835468{col 38}{space 1}    0.86{col 47}{space 3}0.391{col 55}{space 4} .6022549{col 68}{space 3} 3.660397
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 1.234541{col 27}{space 2} .9992786{col 38}{space 1}    0.26{col 47}{space 3}0.795{col 55}{space 4} .2526472{col 68}{space 3} 6.032488
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .7764691{col 27}{space 2} .3602251{col 38}{space 1}   -0.55{col 47}{space 3}0.586{col 55}{space 4} .3127726{col 68}{space 3} 1.927612
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.211109{col 27}{space 2} .6059451{col 38}{space 1}    0.38{col 47}{space 3}0.702{col 55}{space 4} .4542638{col 68}{space 3} 3.228929
{txt}{space 7}female {c |}{col 15}{res}{space 2} 5.555843{col 27}{space 2} 6.308599{col 38}{space 1}    1.51{col 47}{space 3}0.131{col 55}{space 4} .6000948{col 68}{space 3} 51.43752
{txt}{space 8}agein {c |}{col 15}{res}{space 2} .9817301{col 27}{space 2} .0245479{col 38}{space 1}   -0.74{col 47}{space 3}0.461{col 55}{space 4} .9347771{col 68}{space 3} 1.031042
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 2.533411{col 27}{space 2} 1.295673{col 38}{space 1}    1.82{col 47}{space 3}0.069{col 55}{space 4} .9297623{col 68}{space 3} 6.903021
{txt}{space 3}education2 {c |}{col 15}{res}{space 2}  1.04427{col 27}{space 2} .2463768{col 38}{space 1}    0.18{col 47}{space 3}0.854{col 55}{space 4} .6576381{col 68}{space 3} 1.658206
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9163814{col 27}{space 2} .0333318{col 38}{space 1}   -2.40{col 47}{space 3}0.016{col 55}{space 4} .8533266{col 68}{space 3} .9840955
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.399943{col 27}{space 2} .2397947{col 38}{space 1}    1.96{col 47}{space 3}0.050{col 55}{space 4} 1.000712{col 68}{space 3} 1.958447
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} 1.182317{col 27}{space 2} .8915054{col 38}{space 1}    0.22{col 47}{space 3}0.824{col 55}{space 4} .2697119{col 68}{space 3} 5.182837
{txt}{space 10}win {c |}{col 15}{res}{space 2}  1.16544{col 27}{space 2}  .806006{col 38}{space 1}    0.22{col 47}{space 3}0.825{col 55}{space 4} .3004746{col 68}{space 3} 4.520349
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .3900386{col 27}{space 2} .3289559{col 38}{space 1}   -1.12{col 47}{space 3}0.264{col 55}{space 4} .0746809{col 68}{space 3} 2.037067
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} 1.73e-20{col 27}{space 2}        .{col 38}{space 1}       .{col 47}{space 3}    .{col 55}{space 4}        .{col 68}{space 3}        .
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .7915504{col 27}{space 2} .5689278{col 38}{space 1}   -0.33{col 47}{space 3}0.745{col 55}{space 4}  .193498{col 68}{space 3} 3.238028
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} 2.043413    1.48037
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}16.31{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. stset,clear
{txt}
{com}. 
. ******************End Gov******************
. stset tenure_month, failure(endgov==1)

     {txt}failure event:  {res}endgov == 1
{txt}obs. time interval:  {res}(0, tenure_month]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}      1,103{txt}  total observations
{res}         12{txt}  event time missing (tenure_month>=.)               PROBABLE ERROR
{res}          4{txt}  observations end on or before enter()
{hline 78}
{res}      1,087{txt}  observations remaining, representing
{res}        426{txt}  failures in single-record/single-failure data
{res}    872,350{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   14,482
{txt}
{com}. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End, shared(ccode)

         {txt}failure _d:  {res}endgov == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-2156.5079{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-2156.5079{txt}  (backed up)
Iteration 2:{col 16}log profile likelihood = {res}-2156.5067{txt}  
Iteration 3:{col 16}log profile likelihood = {res}-2156.5067{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-2293.0009
{txt}Iteration 1:   log likelihood = {res}-2167.9278
{txt}Iteration 2:   log likelihood = {res}-2156.9235
{txt}Iteration 3:   log likelihood = {res}-2156.5115
{txt}Iteration 4:   log likelihood = {res}-2156.5067
{txt}Iteration 5:   log likelihood = {res}-2156.5067
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-2156.5067

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       912
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         912{col 63}{txt}min = {res}         8
{txt}No. of failures = {res}         394{col 63}{txt}avg = {res} 65.142857
{txt}Time at risk    = {res}      715779{col 63}{txt}max = {res}       163

{col 49}{txt}Wald chi2({res}15{txt}){col 67}= {col 70}{res}   123.31
{txt}Log likelihood  =   {res}-2156.5067{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.438973{col 27}{space 2} .1966897{col 38}{space 1}    2.66{col 47}{space 3}0.008{col 55}{space 4} 1.100788{col 68}{space 3} 1.881054
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} .7592818{col 27}{space 2} .1476772{col 38}{space 1}   -1.42{col 47}{space 3}0.157{col 55}{space 4} .5186182{col 68}{space 3} 1.111625
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .8955401{col 27}{space 2} .1075252{col 38}{space 1}   -0.92{col 47}{space 3}0.358{col 55}{space 4} .7077559{col 68}{space 3} 1.133148
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.004273{col 27}{space 2} .1263374{col 38}{space 1}    0.03{col 47}{space 3}0.973{col 55}{space 4} .7848213{col 68}{space 3} 1.285088
{txt}{space 7}female {c |}{col 15}{res}{space 2} .8499261{col 27}{space 2} .2969395{col 38}{space 1}   -0.47{col 47}{space 3}0.642{col 55}{space 4} .4285446{col 68}{space 3} 1.685646
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.002474{col 27}{space 2} .0068908{col 38}{space 1}    0.36{col 47}{space 3}0.719{col 55}{space 4} .9890589{col 68}{space 3} 1.016071
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.375401{col 27}{space 2} .2336892{col 38}{space 1}    1.88{col 47}{space 3}0.061{col 55}{space 4} .9858363{col 68}{space 3} 1.918906
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} 1.059666{col 27}{space 2} .0766118{col 38}{space 1}    0.80{col 47}{space 3}0.423{col 55}{space 4} .9196631{col 68}{space 3} 1.220982
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} 1.035955{col 27}{space 2} .0108861{col 38}{space 1}    3.36{col 47}{space 3}0.001{col 55}{space 4} 1.014837{col 68}{space 3} 1.057513
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.087823{col 27}{space 2} .0508117{col 38}{space 1}    1.80{col 47}{space 3}0.072{col 55}{space 4} .9926569{col 68}{space 3} 1.192113
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} 1.021389{col 27}{space 2}  .225335{col 38}{space 1}    0.10{col 47}{space 3}0.924{col 55}{space 4} .6628305{col 68}{space 3} 1.573911
{txt}{space 10}win {c |}{col 15}{res}{space 2} .6027484{col 27}{space 2} .0986478{col 38}{space 1}   -3.09{col 47}{space 3}0.002{col 55}{space 4} .4373464{col 68}{space 3} .8307044
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .6484706{col 27}{space 2} .1150932{col 38}{space 1}   -2.44{col 47}{space 3}0.015{col 55}{space 4} .4579474{col 68}{space 3} .9182585
{txt}{space 3}compromise {c |}{col 15}{res}{space 2} .6106444{col 27}{space 2} .1299675{col 38}{space 1}   -2.32{col 47}{space 3}0.020{col 55}{space 4} .4023661{col 68}{space 3} .9267344
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2} .4343016{col 27}{space 2} .0920424{col 38}{space 1}   -3.94{col 47}{space 3}0.000{col 55}{space 4} .2866782{col 68}{space 3} .6579429
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .5944595   .2453087
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}89.78{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. stset,clear
{txt}
{com}. 
. 
. ******************************************
. ******************Appendix 5**************
. ******************************************
. 
. ******************************************
. ******************Table A4****************
. ******************************************
. 
. ******************Ext. Model 5************
. stset tenure_month, failure(forced_resign==1)

     {txt}failure event:  {res}forced_resign == 1
{txt}obs. time interval:  {res}(0, tenure_month]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}      1,103{txt}  total observations
{res}         12{txt}  event time missing (tenure_month>=.)               PROBABLE ERROR
{res}          4{txt}  observations end on or before enter()
{hline 78}
{res}      1,087{txt}  observations remaining, representing
{res}        341{txt}  failures in single-record/single-failure data
{res}    872,350{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   14,482
{txt}
{com}. stcox previous_term politics2 dipexp milexp female agein hog education2 polity2 maxhostlev anyconflict win lose compromise Any_MID_End log_cgdppc, shared(ccode)

         {txt}failure _d:  {res}forced_resign == 1
   {txt}analysis time _t:  {res}tenure_month

{txt}Fitting comparison Cox model:

Estimating frailty variance:

Iteration 0:{col 16}log profile likelihood = {res}-1115.9842{txt}  
Iteration 1:{col 16}log profile likelihood = {res}-1115.9802{txt}  
Iteration 2:{col 16}log profile likelihood = {res}-1115.9802{txt}  

Fitting final Cox model:

Iteration 0:   log likelihood = {res}-1248.1578
{txt}Iteration 1:   log likelihood = {res}-1136.7935
{txt}Iteration 2:   log likelihood = {res}-1116.3324
{txt}Iteration 3:   log likelihood = {res} -1115.981
{txt}Iteration 4:   log likelihood = {res}-1115.9802
{txt}Iteration 5:   log likelihood = {res}-1115.9802
{txt}Refining estimates:
Iteration 0:   log likelihood = {res}-1115.9802

{txt}Cox regression -- Breslow method for ties

Gamma shared frailty{col 49}Number of obs{col 67}={col 69}{res}       762
{txt}Group variable: {res}ccode{col 49}{txt}Number of groups{col 67}={col 69}{res}        14
{col 49}{txt}Obs per group:
No. of subjects = {res}         762{col 63}{txt}min = {res}         1
{txt}No. of failures = {res}         219{col 63}{txt}avg = {res} 54.428571
{txt}Time at risk    = {res}      584969{col 63}{txt}max = {res}       132

{col 49}{txt}Wald chi2({res}16{txt}){col 67}= {col 70}{res}   124.61
{txt}Log likelihood  =   {res}-1115.9802{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           _t{col 15}{c |} Haz. Ratio{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
previous_term {c |}{col 15}{res}{space 2} 1.227737{col 27}{space 2} .2059456{col 38}{space 1}    1.22{col 47}{space 3}0.221{col 55}{space 4} .8837333{col 68}{space 3} 1.705648
{txt}{space 4}politics2 {c |}{col 15}{res}{space 2} 4.109232{col 27}{space 2} 1.655457{col 38}{space 1}    3.51{col 47}{space 3}0.000{col 55}{space 4} 1.865707{col 68}{space 3} 9.050615
{txt}{space 7}dipexp {c |}{col 15}{res}{space 2} .5833273{col 27}{space 2} .0972917{col 38}{space 1}   -3.23{col 47}{space 3}0.001{col 55}{space 4}  .420671{col 68}{space 3} .8088762
{txt}{space 7}milexp {c |}{col 15}{res}{space 2} 1.087056{col 27}{space 2} .1871604{col 38}{space 1}    0.48{col 47}{space 3}0.628{col 55}{space 4} .7757091{col 68}{space 3} 1.523368
{txt}{space 7}female {c |}{col 15}{res}{space 2}  1.83633{col 27}{space 2} .9088339{col 38}{space 1}    1.23{col 47}{space 3}0.219{col 55}{space 4} .6961061{col 68}{space 3} 4.844246
{txt}{space 8}agein {c |}{col 15}{res}{space 2} 1.015186{col 27}{space 2} .0091066{col 38}{space 1}    1.68{col 47}{space 3}0.093{col 55}{space 4} .9974931{col 68}{space 3} 1.033192
{txt}{space 10}hog {c |}{col 15}{res}{space 2} 1.026729{col 27}{space 2} .2381898{col 38}{space 1}    0.11{col 47}{space 3}0.909{col 55}{space 4} .6516081{col 68}{space 3} 1.617802
{txt}{space 3}education2 {c |}{col 15}{res}{space 2} .8435893{col 27}{space 2} .0822469{col 38}{space 1}   -1.74{col 47}{space 3}0.081{col 55}{space 4} .6968543{col 68}{space 3} 1.021222
{txt}{space 6}polity2 {c |}{col 15}{res}{space 2} .9843002{col 27}{space 2} .0155552{col 38}{space 1}   -1.00{col 47}{space 3}0.317{col 55}{space 4} .9542799{col 68}{space 3} 1.015265
{txt}{space 3}maxhostlev {c |}{col 15}{res}{space 2} 1.317928{col 27}{space 2} .0832951{col 38}{space 1}    4.37{col 47}{space 3}0.000{col 55}{space 4} 1.164379{col 68}{space 3} 1.491726
{txt}{space 2}anyconflict {c |}{col 15}{res}{space 2} .7089754{col 27}{space 2} .2079565{col 38}{space 1}   -1.17{col 47}{space 3}0.241{col 55}{space 4} .3989854{col 68}{space 3} 1.259811
{txt}{space 10}win {c |}{col 15}{res}{space 2} .5156053{col 27}{space 2} .1261971{col 38}{space 1}   -2.71{col 47}{space 3}0.007{col 55}{space 4}   .31914{col 68}{space 3} .8330162
{txt}{space 9}lose {c |}{col 15}{res}{space 2} .8015686{col 27}{space 2} .1963781{col 38}{space 1}   -0.90{col 47}{space 3}0.367{col 55}{space 4}   .49591{col 68}{space 3} 1.295623
{txt}{space 3}compromise {c |}{col 15}{res}{space 2}   .48801{col 27}{space 2} .1551983{col 38}{space 1}   -2.26{col 47}{space 3}0.024{col 55}{space 4} .2616539{col 68}{space 3}  .910186
{txt}{space 2}Any_MID_End {c |}{col 15}{res}{space 2}  .377739{col 27}{space 2} .1081985{col 38}{space 1}   -3.40{col 47}{space 3}0.001{col 55}{space 4} .2154646{col 68}{space 3} .6622282
{txt}{space 3}log_cgdppc {c |}{col 15}{res}{space 2} .7015478{col 27}{space 2} .0828651{col 38}{space 1}   -3.00{col 47}{space 3}0.003{col 55}{space 4} .5565645{col 68}{space 3} .8842989
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        theta {c |}  {res} .7487097   .3278151
{txt}{hline 14}{c BT}{hline 64}
LR test of theta=0: {help j_chibar##|_new:chibar2(01) = }{res}95.37{col 56}{txt}Prob >= chibar2 = {res}0.000

{txt}Note: Standard errors of hazard ratios are conditional on theta.

{com}. estat phtest, detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Time
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.00507{col 37}     0.01{col 50}    1{col 64}0.9360
{txt}      politics2{col 19}{c |}{res}{col 25}-0.00963{col 37}     0.03{col 50}    1{col 64}0.8709
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.07609{col 37}     1.39{col 50}    1{col 64}0.2379
{txt}      milexp{col 19}{c |}{res}{col 25} 0.06018{col 37}     0.93{col 50}    1{col 64}0.3358
{txt}      female{col 19}{c |}{res}{col 25} 0.00377{col 37}     0.00{col 50}    1{col 64}0.9553
{txt}      agein{col 19}{c |}{res}{col 25} 0.03453{col 37}     0.36{col 50}    1{col 64}0.5513
{txt}      hog{col 19}{c |}{res}{col 25} 0.01396{col 37}     0.06{col 50}    1{col 64}0.8095
{txt}      education2{col 19}{c |}{res}{col 25}-0.01992{col 37}     0.10{col 50}    1{col 64}0.7570
{txt}      polity2{col 19}{c |}{res}{col 25} 0.06210{col 37}     1.02{col 50}    1{col 64}0.3124
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00404{col 37}     0.00{col 50}    1{col 64}0.9496
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.01303{col 37}     0.04{col 50}    1{col 64}0.8329
{txt}      win{col 19}{c |}{res}{col 25} 0.04596{col 37}     0.50{col 50}    1{col 64}0.4790
{txt}      lose{col 19}{c |}{res}{col 25} 0.06887{col 37}     1.13{col 50}    1{col 64}0.2873
{txt}      compromise{col 19}{c |}{res}{col 25} 0.01475{col 37}     0.05{col 50}    1{col 64}0.8167
{txt}      Any_MID_End{col 19}{c |}{res}{col 25}-0.02589{col 37}     0.15{col 50}    1{col 64}0.6998
{txt}      log_cgdppc{col 19}{c |}{res}{col 25} 0.00923{col 37}     0.02{col 50}    1{col 64}0.8930
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     6.64{col 50}   16{col 64}0.9795
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail log

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.06660{col 37}     1.11{col 50}    1{col 64}0.2917
{txt}      politics2{col 19}{c |}{res}{col 25}-0.00644{col 37}     0.01{col 50}    1{col 64}0.9135
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.07571{col 37}     1.38{col 50}    1{col 64}0.2403
{txt}      milexp{col 19}{c |}{res}{col 25} 0.04348{col 37}     0.48{col 50}    1{col 64}0.4867
{txt}      female{col 19}{c |}{res}{col 25}-0.00281{col 37}     0.00{col 50}    1{col 64}0.9666
{txt}      agein{col 19}{c |}{res}{col 25} 0.06361{col 37}     1.20{col 50}    1{col 64}0.2724
{txt}      hog{col 19}{c |}{res}{col 25} 0.03006{col 37}     0.27{col 50}    1{col 64}0.6036
{txt}      education2{col 19}{c |}{res}{col 25}-0.02450{col 37}     0.14{col 50}    1{col 64}0.7035
{txt}      polity2{col 19}{c |}{res}{col 25} 0.10262{col 37}     2.79{col 50}    1{col 64}0.0951
{txt}      maxhostlev{col 19}{c |}{res}{col 25}-0.00560{col 37}     0.01{col 50}    1{col 64}0.9302
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.04527{col 37}     0.54{col 50}    1{col 64}0.4634
{txt}      win{col 19}{c |}{res}{col 25} 0.01411{col 37}     0.05{col 50}    1{col 64}0.8279
{txt}      lose{col 19}{c |}{res}{col 25} 0.06144{col 37}     0.90{col 50}    1{col 64}0.3425
{txt}      compromise{col 19}{c |}{res}{col 25}-0.01740{col 37}     0.07{col 50}    1{col 64}0.7845
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.01714{col 37}     0.07{col 50}    1{col 64}0.7985
{txt}      log_cgdppc{col 19}{c |}{res}{col 25} 0.01718{col 37}     0.06{col 50}    1{col 64}0.8021
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    14.43{col 50}   16{col 64}0.5669
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail km

{txt}      Test of proportional-hazards assumption

      Time:  {res}Kaplan-Meier
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.01710{col 37}     0.07{col 50}    1{col 64}0.7867
{txt}      politics2{col 19}{c |}{res}{col 25} 0.01038{col 37}     0.03{col 50}    1{col 64}0.8610
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.09184{col 37}     2.03{col 50}    1{col 64}0.1543
{txt}      milexp{col 19}{c |}{res}{col 25} 0.08307{col 37}     1.77{col 50}    1{col 64}0.1840
{txt}      female{col 19}{c |}{res}{col 25} 0.00319{col 37}     0.00{col 50}    1{col 64}0.9621
{txt}      agein{col 19}{c |}{res}{col 25} 0.05557{col 37}     0.92{col 50}    1{col 64}0.3376
{txt}      hog{col 19}{c |}{res}{col 25} 0.02535{col 37}     0.19{col 50}    1{col 64}0.6615
{txt}      education2{col 19}{c |}{res}{col 25}-0.04099{col 37}     0.41{col 50}    1{col 64}0.5242
{txt}      polity2{col 19}{c |}{res}{col 25} 0.08751{col 37}     2.03{col 50}    1{col 64}0.1546
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00796{col 37}     0.02{col 50}    1{col 64}0.9009
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.02990{col 37}     0.23{col 50}    1{col 64}0.6282
{txt}      win{col 19}{c |}{res}{col 25} 0.03973{col 37}     0.37{col 50}    1{col 64}0.5405
{txt}      lose{col 19}{c |}{res}{col 25} 0.07763{col 37}     1.44{col 50}    1{col 64}0.2303
{txt}      compromise{col 19}{c |}{res}{col 25} 0.00194{col 37}     0.00{col 50}    1{col 64}0.9756
{txt}      Any_MID_End{col 19}{c |}{res}{col 25}-0.01820{col 37}     0.07{col 50}    1{col 64}0.7864
{txt}      log_cgdppc{col 19}{c |}{res}{col 25} 0.01585{col 37}     0.05{col 50}    1{col 64}0.8172
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    13.00{col 50}   16{col 64}0.6726
{txt}      {hline 12}{c BT}{hline 51}

{com}. estat phtest, detail rank

{txt}      Test of proportional-hazards assumption

      Time:  {res}Rank(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      previous_t~m{col 19}{c |}{res}{col 25}-0.04464{col 37}     0.50{col 50}    1{col 64}0.4798
{txt}      politics2{col 19}{c |}{res}{col 25} 0.02434{col 37}     0.17{col 50}    1{col 64}0.6813
{txt}      dipexp{col 19}{c |}{res}{col 25} 0.08609{col 37}     1.78{col 50}    1{col 64}0.1818
{txt}      milexp{col 19}{c |}{res}{col 25} 0.07752{col 37}     1.54{col 50}    1{col 64}0.2150
{txt}      female{col 19}{c |}{res}{col 25}-0.00100{col 37}     0.00{col 50}    1{col 64}0.9881
{txt}      agein{col 19}{c |}{res}{col 25} 0.06673{col 37}     1.33{col 50}    1{col 64}0.2495
{txt}      hog{col 19}{c |}{res}{col 25} 0.02895{col 37}     0.25{col 50}    1{col 64}0.6171
{txt}      education2{col 19}{c |}{res}{col 25}-0.05792{col 37}     0.81{col 50}    1{col 64}0.3681
{txt}      polity2{col 19}{c |}{res}{col 25} 0.09373{col 37}     2.32{col 50}    1{col 64}0.1274
{txt}      maxhostlev{col 19}{c |}{res}{col 25} 0.00267{col 37}     0.00{col 50}    1{col 64}0.9666
{txt}      anyconflict{col 19}{c |}{res}{col 25} 0.04906{col 37}     0.63{col 50}    1{col 64}0.4269
{txt}      win{col 19}{c |}{res}{col 25} 0.00856{col 37}     0.02{col 50}    1{col 64}0.8952
{txt}      lose{col 19}{c |}{res}{col 25} 0.06386{col 37}     0.97{col 50}    1{col 64}0.3238
{txt}      compromise{col 19}{c |}{res}{col 25}-0.03615{col 37}     0.32{col 50}    1{col 64}0.5700
{txt}      Any_MID_End{col 19}{c |}{res}{col 25} 0.00521{col 37}     0.01{col 50}    1{col 64}0.9381
{txt}      log_cgdppc{col 19}{c |}{res}{col 25} 0.02049{col 37}     0.09{col 50}    1{col 64}0.7652
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}    16.70{col 50}   16{col 64}0.4050
{txt}      {hline 12}{c BT}{hline 51}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/alex/Dropbox/Papers Essex/FPA Revision/Final Submission/Log_FPA_Replication_War_Performance_Survival_FMs.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 Jun 2020, 21:54:59
{txt}{.-}
{smcl}
{txt}{sf}{ul off}